SAS/STAT 9.2 User's Guide: The MIXED Procedure (Book Excerpt)

SAS/STAT ® 

9.2 User’s Guide 

The MIXED Procedure 

(Book Excerpt) 

SAS ® Documentation

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Chapter 56 

The MIXED Procedure 

Contents 

Overview: MIXED Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3886 

Basic Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3887 

Notation for the Mixed Model . . . . . . . . . . . . . . . . . . . . . . . . . 3888 

PROC MIXED Contrasted with Other SAS Procedures . . . . . . . . . . . . 3889 

Getting Started: MIXED Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 3890 

Clustered Data Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3890 

Syntax: MIXED Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3896 

PROC MIXED Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 3898 

BY Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3910 

CLASS Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3910 

CONTRAST Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3911 

ESTIMATE Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3914 

ID Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3916 

LSMEANS Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3916 

MODEL Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3922 

PARMS Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3937 

PRIOR Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3939 

RANDOM Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3943 

REPEATED Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3948 

WEIGHT Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3962 

Details: MIXED Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3962 

Mixed Models Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3962 

Parameterization of Mixed Models . . . . . . . . . . . . . . . . . . . . . . 3975 

Residuals and Influence Diagnostics . . . . . . . . . . . . . . . . . . . . . . 3980 

Default Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3989 

ODS Table Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3993 

ODS Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3998 

Computational Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4004 

Examples: Mixed Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4008 

Example 56.1: Split-Plot Design . . . . . . . . . . . . . . . . . . . . . . . 4008 

Example 56.2: Repeated Measures . . . . . . . . . . . . . . . . . . . . . . 4013 

Example 56.3: Plotting the Likelihood . . . . . . . . . . . . . . . . . . . . 4026 

Example 56.4: Known G and R . . . . . . . . . . . . . . . . . . . . . . . . 4033 

Example 56.5: Random Coefficients . . . . . . . . . . . . . . . . . . . . . 4041

3886 ✦ Chapter 56: The MIXED Procedure 

Example 56.6: Line-Source Sprinkler Irrigation . . . . . . . . . . . . . . . 4049 

Example 56.7: Influence in Heterogeneous Variance Model . . . . . . . . . 4055 

Example 56.8: Influence Analysis for Repeated Measures Data . . . . . . . 4064 

Example 56.9: Examining Individual Test Components . . . . . . . . . . . 4073 

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4078 

Overview: MIXED Procedure 

The MIXED procedure fits a variety of mixed linear models to data and enables you to use these 

fitted models to make statistical inferences about the data. A mixed linear model is a generalization 

of the standard linear model used in the GLM procedure, the generalization being that the data 

are permitted to exhibit correlation and nonconstant variability. The mixed linear model, therefore, 

provides you with the flexibility of modeling not only the means of your data (as in the standard 

linear model) but their variances and covariances as well. 

The primary assumptions underlying the analyses performed by PROC MIXED are as follows: 

The data are normally distributed (Gaussian). 

The means (expected values) of the data are linear in terms of a certain set of parameters. 

The variances and covariances of the data are in terms of a different set of parameters, and 

they exhibit a structure matching one of those available in PROC MIXED. 

Since Gaussian data can be modeled entirely in terms of their means and variances/covariances, the 

two sets of parameters in a mixed linear model actually specify the complete probability distribution 

of the data. The parameters of the mean model are referred to as fixed-effects parameters, and the 

parameters of the variance-covariance model are referred to as covariance parameters. 

The fixed-effects parameters are associated with known explanatory variables, as in the standard 

linear model. These variables can be either qualitative (as in the traditional analysis of variance) 

or quantitative (as in standard linear regression). However, the covariance parameters are what 

distinguishes the mixed linear model from the standard linear model. 

The need for covariance parameters arises quite frequently in applications, the following being the 

two most typical scenarios: 

The experimental units on which the data are measured can be grouped into clusters, and the 

data from a common cluster are correlated. 

Repeated measurements are taken on the same experimental unit, and these repeated measurements 

are correlated or exhibit variability that changes. 

The first scenario can be generalized to include one set of clusters nested within another. For example, 

if students are the experimental unit, they can be clustered into classes, which in turn can be

Basic Features ✦ 3887 

clustered into schools. Each level of this hierarchy can introduce an additional source of variability 

and correlation. The second scenario occurs in longitudinal studies, where repeated measurements 

are taken over time. Alternatively, the repeated measures could be spatial or multivariate in nature. 

PROC MIXED provides a variety of covariance structures to handle the previous two scenarios. 

The most common of these structures arises from the use of random-effects parameters, which are 

additional unknown random variables assumed to affect the variability of the data. The variances of 

the random-effects parameters, commonly known as variance components, become the covariance 

parameters for this particular structure. Traditional mixed linear models contain both fixed- and 

random-effects parameters, and, in fact, it is the combination of these two types of effects that led 

to the name mixed model. PROC MIXED fits not only these traditional variance component models 

but numerous other covariance structures as well. 

PROC MIXED fits the structure you select to the data by using the method of restricted maximum 

likelihood (REML), also known as residual maximum likelihood. It is here that the Gaussian assumption 

for the data is exploited. Other estimation methods are also available, including maximum 

likelihood and MIVQUE0. The details behind these estimation methods are discussed in subsequent 

sections. 

After a model has been fit to your data, you can use it to draw statistical inferences via both the fixedeffects 

and covariance parameters. PROC MIXED computes several different statistics suitable for 

generating hypothesis tests and confidence intervals. The validity of these statistics depends upon 

the mean and variance-covariance model you select, so it is important to choose the model carefully. 

Some of the output from PROC MIXED helps you assess your model and compare it with others. 

Basic Features 

PROC MIXED provides easy accessibility to numerous mixed linear models that are useful in many 

common statistical analyses. In the style of the GLM procedure, PROC MIXED fits the specified 

mixed linear model and produces appropriate statistics. 

Here are some basic features of PROC MIXED: 

covariance structures, including variance components, compound symmetry, unstructured, 

AR(1), Toeplitz, spatial, general linear, and factor analytic 

GLM-type grammar, by using MODEL, RANDOM, and REPEATED statements for model 

specification and CONTRAST, ESTIMATE, and LSMEANS statements for inferences 

appropriate standard errors for all specified estimable linear combinations of fixed and random 

effects, and corresponding t and F tests 

subject and group effects that enable blocking and heterogeneity, respectively 

REML and ML estimation methods implemented with a Newton-Raphson algorithm 

capacity to handle unbalanced data 

ability to create a SAS data set corresponding to any table


PROC MIXED uses the Output Delivery System (ODS), a SAS subsystem that provides capabilities 

for displaying and controlling the output from SAS procedures. ODS enables you to convert any 

of the output from PROC MIXED into a SAS data set. See the section “ODS Table Names” on 

page 3993. 

The MIXED procedure now uses ODS Graphics to create graphs as part of its output. For general 

information about ODS Graphics, see Chapter 21, “Statistical Graphics Using ODS.” For specific 

information about the statistical graphics available with the MIXED procedure, see the PLOTS 

option in the PROC MIXED statement and the section “ODS Graphics” on page 3998. 

Notation for the Mixed Model 

This section introduces the mathematical notation used throughout this chapter to describe the 

mixed linear model. You should be familiar with basic matrix algebra (see Searle 1982). A more 

detailed description of the mixed model is contained in the section “Mixed Models Theory” on 

page 3962. 

A statistical model is a mathematical description of how data are generated. The standard linear 

model, as used by the GLM procedure, is one of the most common statistical models: 

y D Xˇ C 

In this expression, y represents a vector of observed data, ˇ is an unknown vector of fixed-effects 

parameters with known design matrix X, and is an unknown random error vector modeling the 

statistical noise around Xˇ. The focus of the standard linear model is to model the mean of y 

by using the fixed-effects parameters ˇ. The residual errors are assumed to be independent and 

identically distributed Gaussian random variables with mean 0 and variance 2 . 

The mixed model generalizes the standard linear model as follows: 

y D Xˇ C Z C 

Here, is an unknown vector of random-effects parameters with known design matrix Z, and 

is an unknown random error vector whose elements are no longer required to be independent and 

homogeneous. 

To further develop this notion of variance modeling, assume that and are Gaussian random 

variables that are uncorrelated and have expectations 0 and variances G and R, respectively. The 

variance of y is thus 

V D ZGZ 0 C R 

Note that, when R D 2 I and Z D 0, the mixed model reduces to the standard linear model. 

You can model the variance of the data, y, by specifying the structure (or form) of Z, G, and R. The 

model matrix Z is set up in the same fashion as X, the model matrix for the fixed-effects parameters. 

For G and R, you must select some covariance structure. Possible covariance structures include the 

following:

variance components 

compound symmetry (common covariance plus diagonal) 

unstructured (general covariance) 

autoregressive 

spatial 

general linear 

factor analytic 

PROC MIXED Contrasted with Other SAS Procedures ✦ 3889 

By appropriately defining the model matrices X and Z, as well as the covariance structure matrices 

G and R, you can perform numerous mixed model analyses. 

PROC MIXED Contrasted with Other SAS Procedures 

PROC MIXED is a generalization of the GLM procedure in the sense that PROC GLM fits standard 

linear models, and PROC MIXED fits the wider class of mixed linear models. Both procedures 

have similar CLASS, MODEL, CONTRAST, ESTIMATE, and LSMEANS statements, but their 

RANDOM and REPEATED statements differ (see the following paragraphs). Both procedures use 

the non-full-rank model parameterization, although the sorting of classification levels can differ 

between the two. PROC MIXED computes only Type I–Type III tests of fixed effects, while PROC 

GLM computes Types I–IV. 

The RANDOM statement in PROC MIXED incorporates random effects constituting the vector 

in the mixed model. However, in PROC GLM, effects specified in the RANDOM statement are still 

treated as fixed as far as the model fit is concerned, and they serve only to produce corresponding 

expected mean squares. These expected mean squares lead to the traditional ANOVA estimates of 

variance components. PROC MIXED computes REML and ML estimates of variance parameters, 

which are generally preferred to the ANOVA estimates (Searle 1988; Harville 1988; Searle, Casella, 

and McCulloch 1992). Optionally, PROC MIXED also computes MIVQUE0 estimates, which are 

similar to ANOVA estimates. 

The REPEATED statement in PROC MIXED is used to specify covariance structures for repeated 

measurements on subjects, while the REPEATED statement in PROC GLM is used to specify various 

transformations with which to conduct the traditional univariate or multivariate tests. In repeated 

measures situations, the mixed model approach used in PROC MIXED is more flexible and 

more widely applicable than either the univariate or multivariate approach. In particular, the mixed 

model approach provides a larger class of covariance structures and a better mechanism for handling 

missing values (Wolfinger and Chang 1995). 

PROC MIXED subsumes the VARCOMP procedure. PROC MIXED provides a wide variety of covariance 

structures, while PROC VARCOMP estimates only simple random effects. PROC MIXED 

carries out several analyses that are absent in PROC VARCOMP, including the estimation and testing 

of linear combinations of fixed and random effects.


The ARIMA and AUTOREG procedures provide more time series structures than PROC MIXED, 

although they do not fit variance component models. The CALIS procedure fits general covariance 

matrices, but the fixed effects structure of the model is formed differently than in PROC MIXED. 

The LATTICE and NESTED procedures fit special types of mixed linear models that can also be 

handled in PROC MIXED, although PROC MIXED might run slower because of its more general 

algorithm. The TSCSREG procedure analyzes time series cross-sectional data, and it fits some 

structures not available in PROC MIXED. 

The GLIMMIX procedure fits generalized linear mixed models (GLMMs). Linear mixed models— 

where the data are normally distributed, given the random effects—are in the class of GLMMs. The 

MIXED procedure can estimate covariance parameters with ANOVA methods that are not available 

in the GLIMMIX procedure (see METHOD=TYPE1, METHOD=TYPE2, and METHOD=TYPE3 

in the PROC MIXED statement). Also, PROC MIXED can perform a sampling-based Bayesian 

analysis through the PRIOR statement, and the procedure supports certain Kronecker-type covariance 

structures. These features are not available in the GLIMMIX procedure. The GLIMMIX 

procedure, on the other hand, accommodates nonnormal data and offers a broader array of postprocessing 

features than the MIXED procedure. 

Getting Started: MIXED Procedure 

Clustered Data Example 

Consider the following SAS data set as an introductory example: 

data heights; 

input Family Gender$ Height @@; 

datalines; 

1 F 67 1 F 66 1 F 64 1 M 71 1 M 72 2 F 63 

2 F 63 2 F 67 2 M 69 2 M 68 2 M 70 3 F 63 

3 M 64 4 F 67 4 F 66 4 M 67 4 M 67 4 M 69 

; 

The response variable Height measures the heights (in inches) of 18 individuals. The individuals 

are classified according to Family and Gender. You can perform a traditional two-way analysis of 

variance of these data with the following PROC MIXED statements: 

proc mixed data=heights; 

class Family Gender; 

model Height = Gender Family Family*Gender; 

run; 

The PROC MIXED statement invokes the procedure. The CLASS statement instructs PROC 

MIXED to consider both Family and Gender as classification variables. Dummy (indicator) variables 

are, as a result, created corresponding to all of the distinct levels of Family and Gender. For 

these data, Family has four levels and Gender has two levels.

Clustered Data Example ✦ 3891 

The MODEL statement first specifies the response (dependent) variable Height. The explanatory 

(independent) variables are then listed after the equal (=) sign. Here, the two explanatory variables 

are Gender and Family, and these are the main effects of the design. The third explanatory term, 

Family*Gender, models an interaction between the two main effects. 

PROC MIXED uses the dummy variables associated with Gender, Family, and Family*Gender to 

construct the X matrix for the linear model. A column of 1s is also included as the first column of 

X to model a global intercept. There are no Z or G matrices for this model, and R is assumed to 

equal 2 I, where I is an 18 18 identity matrix. 

The RUN statement completes the specification. The coding is precisely the same as with the GLM 

procedure. However, much of the output from PROC MIXED is different from that produced by 

PROC GLM. 

The output from PROC MIXED is shown in Figure 56.1–Figure 56.7. 

The “Model Information” table in Figure 56.1 describes the model, some of the variables that it 

involves, and the method used in fitting it. This table also lists the method (profile, factor, parameter, 

or none) for handling the residual variance. 

Figure 56.1 Model Information 

The Mixed Procedure 

Model Information 

Data Set WORK.HEIGHTS 

Dependent Variable Height 

Covariance Structure Diagonal 

Estimation Method REML 

Residual Variance Method Profile 

Fixed Effects SE Method Model-Based 

Degrees of Freedom Method Residual 

The “Class Level Information” table in Figure 56.2 lists the levels of all variables specified in the 

CLASS statement. You can check this table to make sure that the data are correct. 

Figure 56.2 Class Level Information 

Class Level Information 

Class Levels Values 

Family 4 1 2 3 4 

Gender 2 F M 

The “Dimensions” table in Figure 56.3 lists the sizes of relevant matrices. This table can be useful 

in determining CPU time and memory requirements.


Figure 56.3 Dimensions 

Dimensions 

Covariance Parameters 1 

Columns in X 15 

Columns in Z 0 

Subjects 1 

Max Obs Per Subject 18 

The “Number of Observations” table in Figure 56.4 displays information about the sample size 

being processed. 

Figure 56.4 Number of Observations 

Number of Observations 

Number of Observations Read 18 

Number of Observations Used 18 

Number of Observations Not Used 0 

The “Covariance Parameter Estimates” table in Figure 56.5 displays the estimate of 2 for the 

model. 

Figure 56.5 Covariance Parameter Estimates 

Covariance Parameter 

Estimates 

Cov Parm Estimate 

Residual 2.1000 

The “Fit Statistics” table in Figure 56.6 lists several pieces of information about the fitted mixed 

model, including values derived from the computed value of the restricted/residual likelihood. 

Figure 56.6 Fit Statistics 

Fit Statistics 

-2 Res Log Likelihood 41.6 

AIC (smaller is better) 43.6 

AICC (smaller is better) 44.1 

BIC (smaller is better) 43.9 

The “Type 3 Tests of Fixed Effects” table in Figure 56.7 displays significance tests for the three 

effects listed in the MODEL statement. The Type 3 F statistics and p-values are the same as those 

produced by the GLM procedure. However, because PROC MIXED uses a likelihood-based esti-


mation scheme, it does not directly compute or display sums of squares for this analysis. 

Figure 56.7 Tests of Fixed Effects 

Type 3 Tests of Fixed Effects 

Num Den 

Effect DF DF F Value Pr > F 

Gender 1 10 17.63 0.0018 

Family 3 10 5.90 0.0139 

Family*Gender 3 10 2.89 0.0889 

The Type 3 test for Family*Gender effect is not significant at the 5% level, but the tests for both main 

effects are significant. 

The important assumptions behind this analysis are that the data are normally distributed and that 

they are independent with constant variance. For these data, the normality assumption is probably 

realistic since the data are observed heights. However, since the data occur in clusters (families), 

it is very likely that observations from the same family are statistically correlated—that is, not 

independent. 

The methods implemented in PROC MIXED are still based on the assumption of normally distributed 

data, but you can drop the assumption of independence by modeling statistical correlation 

in a variety of ways. You can also model variances that are heterogeneous—that is, nonconstant. 

For the height data, one of the simplest ways of modeling correlation is through the use of random 

effects. Here the family effect is assumed to be normally distributed with zero mean and some 

unknown variance. This is in contrast to the previous model in which the family effects are just 

constants, or fixed effects. Declaring Family as a random effect sets up a common correlation among 

all observations having the same level of Family. 

Declaring Family*Gender as a random effect models an additional correlation between all observations 

that have the same level of both Family and Gender. One interpretation of this effect is that a 

female in a certain family exhibits more correlation with the other females in that family than with 

the other males, and likewise for a male. With the height data, this model seems reasonable. 

The statements to fit this correlation model in PROC MIXED are as follows: 

proc mixed; 


model Height = Gender; 

random Family Family*Gender; 

run; 

Note that Family and Family*Gender are now listed in the RANDOM statement. The dummy variables 

associated with them are used to construct the Z matrix in the mixed model. The X matrix 

now consists of a column of 1s and the dummy variables for Gender. 

The G matrix for this model is diagonal, and it contains the variance components for both Family 

and Family*Gender. The R matrix is still assumed to equal 2 I, where I is an identity matrix.


The output from this analysis is as follows. 

Figure 56.8 Model Information 



Data Set WORK.HEIGHTS 

Dependent Variable Height 

Covariance Structure Variance Components 




Degrees of Freedom Method Containment 

The “Model Information” table in Figure 56.8 shows that the containment method is used to compute 

the degrees of freedom for this analysis. This is the default method when a RANDOM statement 

is used; see the description of the DDFM= option for more information. 

Figure 56.9 Class Level Information 



Family 4 1 2 3 4 

Gender 2 F M 

The “Class Level Information” table in Figure 56.9 is the same as before. The “Dimensions” table 

in Figure 56.10 displays the new sizes of the X and Z matrices. 

Figure 56.10 Dimensions and Number of Observations 

Dimensions 




Subjects 1 






The “Iteration History” table in Figure 56.11 displays the results of the numerical optimization 

of the restricted/residual likelihood. Six iterations are required to achieve the default convergence

criterion of 1E 8. 

Figure 56.11 REML Estimation Iteration History 

Iteration History 

Iteration Evaluations -2 Res Log Like Criterion 

0 1 74.11074833 

1 2 71.51614003 0.01441208 

2 1 71.13845990 0.00412226 

3 1 71.03613556 0.00058188 

4 1 71.02281757 0.00001689 

5 1 71.02245904 0.00000002 

6 1 71.02245869 0.00000000 

Convergence criteria met. 


The “Covariance Parameter Estimates” table in Figure 56.12 displays the results of the REML 

fit. The Estimate column contains the estimates of the variance components for Family and Family*Gender, 

as well as the estimate of 2 . 

Figure 56.12 Covariance Parameter Estimates (REML) 


Estimates 


Family 2.4010 

Family*Gender 1.7657 


The “Fit Statistics” table in Figure 56.13 contains basic information about the REML fit. 

Figure 56.13 Fit Statistics 






The “Type 3 Tests of Fixed Effects” table in Figure 56.14 contains a significance test for the lone 

fixed effect, Gender. Note that the associated p-value is not nearly as significant as in the previous 

analysis. This illustrates the importance of correctly modeling correlation in your data.


Figure 56.14 Type 3 Tests of Fixed Effects 


Num Den 


Gender 1 3 7.95 0.0667 

An additional benefit of the random effects analysis is that it enables you to make inferences about 

gender that apply to an entire population of families, whereas the inferences about gender from the 

analysis where Family and Family*Gender are fixed effects apply only to the particular families in the 

data set. 

PROC MIXED thus offers you the ability to model correlation directly and to make inferences about 

fixed effects that apply to entire populations of random effects. 

Syntax: MIXED Procedure 

The following statements are available in PROC MIXED. 

PROC MIXED < options > ; 

BY variables ; 

CLASS variables ; 

ID variables ; 

MODEL dependent = < fixed-effects > < / options > ; 

RANDOM random-effects < / options > ; 

REPEATED < repeated-effect >< / options > ; 

PARMS (value-list) . . . < / options > ; 

PRIOR < distribution >< / options > ; 

CONTRAST ’label’ < fixed-effect values . . . > 

< | random-effect values . . . >, . . . < / options > ; 

ESTIMATE ’label’ < fixed-effect values . . . > 

< | random-effect values . . . >< / options > ; 

LSMEANS fixed-effects < / options > ; 

WEIGHT variable ; 

Items within angle brackets ( < > ) are optional. The CONTRAST, ESTIMATE, LSMEANS, and 

RANDOM statements can appear multiple times; all other statements can appear only once. 

The PROC MIXED and MODEL statements are required, and the MODEL statement must appear 

after the CLASS statement if a CLASS statement is included. The CONTRAST, ESTIMATE, 

LSMEANS, RANDOM, and REPEATED statements must follow the MODEL statement. The 

CONTRAST and ESTIMATE statements must also follow any RANDOM statements. 

Table 56.1 summarizes the basic functions and important options of each PROC MIXED statement.

Syntax: MIXED Procedure ✦ 3897 

The syntax of each statement in Table 56.1 is described in the following sections in alphabetical 

order after the description of the PROC MIXED statement. 

Table 56.1 Summary of PROC MIXED Statements 

Statement Description Important Options 

PROC MIXED invokes the procedure DATA= specifies input data set, METHOD= specifies 

estimation method 

BY performs multiple 

PROC MIXED analyses 

in one invocation 

none 

CLASS declares qualitative variables 

that create indicator 

variables in design 

matrices 

none 

ID lists additional variables 

to be included in predicted 

values tables 

none 

MODEL specifies dependent vari- S requests solution for fixed-effects parameters, 

able and fixed effects, DDFM= specifies denominator degrees of free- 

setting up X 

dom method, OUTP= outputs predicted values to 

a data set, INFLUENCE computes influence diagnostics 

RANDOM specifies random effects, SUBJECT= creates block-diagonality, TYPE= 

setting up Z and G specifies covariance structure, S requests solution 

for random-effects parameters, G displays estimated 

G 

REPEATED sets up R SUBJECT= creates block-diagonality, TYPE= 

specifies covariance structure, R displays estimated 

blocks of R, GROUP= enables betweensubject 

heterogeneity, LOCAL adds a diagonal 

matrix to R 

PARMS specifies a grid of initial HOLD= and NOITER hold the covariance pa- 

values for the covariance rameters or their ratios constant, PARMSDATA= 

parameters 

reads the initial values from a SAS data set 

PRIOR performs a sampling- NSAMPLE= specifies the sample size, SEED= 

based Bayesian analysis 

for variance component 

models 

specifies the starting seed 

CONTRAST constructs custom hy- E displays the L matrix coefficients 

ESTIMATE 

pothesis tests 

constructs custom scalar 

estimates 

CL produces confidence limits 

LSMEANS computes least squares DIFF computes differences of the least squares 

means for classification means, ADJUST= performs multiple compar- 

fixed effects 

isons adjustments, AT changes covariates, OM 

changes weighting, CL produces confidence limits, 

SLICE= tests simple effects


Table 56.1 continued 

Statement Description Important Options 

WEIGHT specifies a variable by 

which to weight R 

PROC MIXED Statement 

PROC MIXED < options > ; 

none 

The PROC MIXED statement invokes the procedure. Table 56.2 summarizes important options in 

the PROC MIXED statement by function. These and other options in the PROC MIXED statement 

are then described fully in alphabetical order. 

Table 56.2 PROC MIXED Statement Options 

Option Description 

Basic Options 

DATA= specifies input data set 

METHOD= specifies the estimation method 

NOPROFILE includes scale parameter in optimization 

ORDER= determines the sort order of CLASS variables 

Displayed Output 

ASYCORR displays asymptotic correlation matrix of covariance parameter estimates 

ASYCOV displays asymptotic covariance matrix of covariance parameter estimates 

CL requests confidence limits for covariance parameter estimates 

COVTEST displays asymptotic standard errors and Wald tests for covariance 

parameters 

IC displays a table of information criteria 

ITDETAILS displays estimates and gradients added to “Iteration History” 

LOGNOTE writes periodic status notes to the log 

MMEQ displays mixed model equations 

MMEQSOL displays the solution to the mixed model equations 

NOCLPRINT suppresses “Class Level Information” completely or in parts 

NOITPRINT suppresses “Iteration History” table 

PLOTS produces ODS statistical graphics 

RATIO produces ratio of covariance parameter estimates with residual 

variance 

Optimization Options 

MAXFUNC= specifies the maximum number of likelihood evaluations 

MAXITER= specifies the maximum number of iterations



PROC MIXED Statement ✦ 3899 

Computational Options 

CONVF requests and tunes the relative function convergence criterion 

CONVG requests and tunes the relative gradient convergence criterion 

CONVH requests and tunes the relative Hessian convergence criterion 

DFBW selects between-within degree of freedom method 

EMPIRICAL computes empirical (“sandwich”) estimators 

NOBOUND unbounds covariance parameter estimates 

RIDGE= specifies starting value for minimum ridge value 

SCORING= applies Fisher scoring where applicable 

You can specify the following options. 

ABSOLUTE 

makes the convergence criterion absolute. By default, it is relative (divided by the current 

objective function value). See the CONVF, CONVG, and CONVH options in this section for 

a description of various convergence criteria. 

ALPHA=number 

requests that confidence limits be constructed for the covariance parameter estimates with 

confidence level 1 number. The value of number must be between 0 and 1; the default is 

0.05. 

ANOVAF 

The ANOVAF option computes F tests in models with REPEATED statement and without 

RANDOM statement by a method similar to that of Brunner, Domhof, and Langer (2002). 

The method consists of computing special F statistics and adjusting their degrees of freedom. 

The technique is a generalization of the Greenhouse-Geiser adjustment in MANOVA models 

(Greenhouse and Geiser 1959). For more details, see the section “F Tests With the ANOVAF 

Option” on page 3973. 

ASYCORR 

produces the asymptotic correlation matrix of the covariance parameter estimates. It is 

computed from the corresponding asymptotic covariance matrix (see the description of the 

ASYCOV option, which follows). For ODS purposes, the name of the “Asymptotic Correlation” 

table is “AsyCorr.” 

ASYCOV 

requests that the asymptotic covariance matrix of the covariance parameters be displayed. By 

default, this matrix is the observed inverse Fisher information matrix, which equals 2H 1 , 

where H is the Hessian (second derivative) matrix of the objective function. See the section 

“Covariance Parameter Estimates” on page 3991 for more information about this matrix. 

When you use the SCORING= option and PROC MIXED converges without stopping the 

scoring algorithm, PROC MIXED uses the expected Hessian matrix to compute the covariance 

matrix instead of the observed Hessian. For ODS purposes, the name of the “Asymptotic 

Covariance” table is “AsyCov.”


CL< =WALD > 

requests confidence limits for the covariance parameter estimates. A Satterthwaite approximation 

is used to construct limits for all parameters that have a lower boundary constraint of 

zero. These limits take the form 

b2 2 

;1 ˛=2 

2 

b 2 

2 

;˛=2 

where D 2Z 2 , Z is the Wald statistic b 2 =se.b 2 /, and the denominators are quantiles of 

the 2 -distribution with degrees of freedom. See Milliken and Johnson (1992) and Burdick 

and Graybill (1992) for similar techniques. 

For all other parameters, Wald Z-scores and normal quantiles are used to construct the limits. 

Wald limits are also provided for variance components if you specify the NOBOUND option. 

The optional =WALD specification requests Wald limits for all parameters. 

The confidence limits are displayed as extra columns in the “Covariance Parameter Estimates” 

table. The confidence level is 1 ˛ D 0:95 by default; this can be changed with the ALPHA= 

option. 

CONVF< =number > 

requests the relative function convergence criterion with tolerance number. The relative function 

convergence criterion is 

jf k f k 1j 

jf kj 

number 

where f k is the value of the objective function at iteration k. To prevent the division by jf kj, 

use the ABSOLUTE option. The default convergence criterion is CONVH, and the default 

tolerance is 1E 8. 

CONVG < =number > 

requests the relative gradient convergence criterion with tolerance number. The relative gradient 


maxj jg jkj 

jf kj 

number 

where f k is the value of the objective function, and g jk is the j th element of the gradient 

(first derivative) of the objective function, both at iteration k. To prevent division by jf kj, 

use the ABSOLUTE option. The default convergence criterion is CONVH, and the default 

tolerance is 1E 8. 

CONVH< =number > 

requests the relative Hessian convergence criterion with tolerance number. The relative Hessian 


g k 0 H 1 

k g k 

jf kj 

number 

where f k is the value of the objective function, g k is the gradient (first derivative) of the 

objective function, and H k is the Hessian (second derivative) of the objective function, all at 

iteration k.

If H k is singular, then PROC MIXED uses the following relative criterion: 

g 0 

k g k 

jf kj 

number 


To prevent the division by jf kj, use the ABSOLUTE option. The default convergence criterion 

is CONVH, and the default tolerance is 1E 8. 

COVTEST 

produces asymptotic standard errors and Wald Z-tests for the covariance parameter estimates. 

DATA=SAS-data-set 

names the SAS data set to be used by PROC MIXED. The default is the most recently created 

data set. 

DFBW 

has the same effect as the DDFM=BW option in the MODEL statement. 

EMPIRICAL 

computes the estimated variance-covariance matrix of the fixed-effects parameters by using 

the asymptotically consistent estimator described in Huber (1967), White (1980), Liang and 

Zeger (1986), and Diggle, Liang, and Zeger (1994). This estimator is commonly referred to 

as the “sandwich” estimator, and it is computed as follows: 

IC 

.X 0bV 1 X/ 

SX 

iD1 

X 0 i cVi 1 bi bi 0cVi 1 Xi 

! 

.X 0bV 1 X/ 

Here, bi D yi Xi bˇ, S is the number of subjects, and matrices with an i subscript are 

those for the ith subject. You must include the SUBJECT= option in either a RANDOM or 

REPEATED statement for this option to take effect. 

When you specify the EMPIRICAL option, PROC MIXED adjusts all standard errors and test 

statistics involving the fixed-effects parameters. This changes output in the following tables 

(listed in Table 56.22): Contrast, CorrB, CovB, Diffs, Estimates, InvCovB, LSMeans, Slices, 

SolutionF, Tests1–Tests3. The OUTP= and OUTPM= data sets are also affected. Finally, 

the Satterthwaite and Kenward-Roger degrees of freedom methods are not available if you 

specify the EMPIRICAL option. 

displays a table of various information criteria. The criteria are all in smaller-is-better form, 

and are described in Table 56.3. 

Table 56.3 Information Criteria 

Criterion Formula Reference 

AIC 2` C 2d Akaike (1974) 

AICC 2` C 2dn =.n d 1/ Hurvich and Tsai (1989) 

Burnham and Anderson (1998) 

HQIC 2` C 2d log log n Hannan and Quinn (1979) 

BIC 2` C d log n Schwarz (1978) 

CAIC 2` C d.log n C 1/ Bozdogan (1987)


INFO 

Here ` denotes the maximum value of the (possibly restricted) log likelihood, d the dimension 

of the model, and n the number of observations. In SAS 6 of SAS/STAT software, n equals 

the number of valid observations for maximum likelihood estimation and n p for restricted 

maximum likelihood estimation, where p equals the rank of X. In later versions, n equals the 

number of effective subjects as displayed in the “Dimensions” table, unless this value equals 

1, in which case n equals the number of levels of the first random effect you specify in a 

RANDOM statement. If the number of effective subjects equals 1 and you have no RANDOM 

statements, then n reverts to the SAS 6 values. For AICC (a finite-sample corrected version 

of AIC), n equals the SAS 6 values of n, unless this number is less than d C 2, in which 

case it equals d C 2. 

For restricted likelihood estimation, d equals q, the effective number of estimated covariance 

parameters. In SAS 6, when a parameter estimate lies on a boundary constraint, then it is still 

included in the calculation of d, but in later versions it is not. The most common example 

of this behavior is when a variance component is estimated to equal zero. For maximum 

likelihood estimation, d equals q C p. 

For ODS purposes, the name of the “Information Criteria” table is “InfoCrit.” 

is a default option. The creation of the “Model Information,” “Dimensions,” and “Number of 

Observations” tables can be suppressed by using the NOINFO option. 

Note that in SAS 6 this option displays the “Model Information” and “Dimensions” tables. 

ITDETAILS 

displays the parameter values at each iteration and enables the writing of notes to the SAS log 

pertaining to “infinite likelihood” and “singularities” during Newton-Raphson iterations. 

LOGNOTE 

writes periodic notes to the log describing the current status of computations. It is designed 

for use with analyses requiring extensive CPU resources. 

MAXFUNC=number 

specifies the maximum number of likelihood evaluations in the optimization process. The 

default is 150. 

MAXITER=number 

specifies the maximum number of iterations. The default is 50. 

METHOD=REML 

METHOD=ML 

METHOD=MIVQUE0 

METHOD=TYPE1 

METHOD=TYPE2 

METHOD=TYPE3 

specifies the estimation method for the covariance parameters. The REML specification performs 

residual (restricted) maximum likelihood, and it is the default method. The ML specification 

performs maximum likelihood, and the MIVQUE0 specification performs minimum 

variance quadratic unbiased estimation of the covariance parameters.

MMEQ 


The METHOD=TYPEn specifications apply only to variance component models with no 

SUBJECT= effects and no REPEATED statement. An analysis of variance table is included 

in the output, and the expected mean squares are used to estimate the variance components 

(see Chapter 39, “The GLM Procedure,” for further explanation). The resulting method-ofmoment 

variance component estimates are used in subsequent calculations, including standard 

errors computed from ESTIMATE and LSMEANS statements. For ODS purposes, the 

new table names are “Type1,” “Type2,” and “Type3,” respectively. 

requests that coefficients of the mixed model equations be displayed. These are 

" 

X 0bR 1X X 0bR 1Z Z 0bR 1X Z 0bR 1Z C bG 1 

# " 

X 

; 

0bR 1y Z 0bR 1 # 

y 

assuming that bG is nonsingular. If bG is singular, PROC MIXED produces the following 

coefficients: 

" 

X 0bR 1 X X 0bR 1 ZbG 

bGZ 0bR 1 X bGZ 0bR 1 ZbG C bG 

# 

; 

" 

X 0bR 1 y 

bGZ 0bR 1 y 

See the section “Estimating Fixed and Random Effects in the Mixed Model” on page 3970 

for further information about these equations. 

MMEQSOL 

requests that a solution to the mixed model equations be produced, as well as the inverted 

coefficients matrix. Formulas for these equations are provided in the preceding description of 

the MMEQ option. 

When bG is singular, b and a generalized inverse of the left-hand-side coefficient matrix are 

transformed by using bG to produce b and bC, respectively, where bC is a generalized inverse 

of the left-hand-side coefficient matrix of the original equations. 

NAMELEN< =number > 

specifies the length to which long effect names are shortened. The default and minimum value 

is 20. 

NOBOUND 

has the same effect as the NOBOUND option in the PARMS statement. 

NOCLPRINT< =number > 

suppresses the display of the “Class Level Information” table if you do not specify number. 

If you do specify number, only levels with totals that are less than number are listed in the 

table. 

NOINFO 

suppresses the display of the “Model Information,” “Dimensions,” and “Number of Observations” 

tables. 

NOITPRINT 

suppresses the display of the “Iteration History” table. 

#


NOPROFILE 

includes the residual variance as part of the Newton-Raphson iterations. This option applies 

only to models that have a residual variance parameter. By default, this parameter is profiled 

out of the likelihood calculations, except when you have specified the HOLD= option in the 

PARMS statement. 

ORD 

ORDER=DATA 

displays ordinates of the relevant distribution in addition to p-values. The ordinate can be 

viewed as an approximate odds ratio of hypothesis probabilities. 

ORDER=FORMATTED 

ORDER=FREQ 

ORDER=INTERNAL 

specifies the sorting order for the levels of all CLASS variables. This ordering determines 

which parameters in the model correspond to each level in the data, so the ORDER= option 

can be useful when you use CONTRAST or ESTIMATE statements. 

The default is ORDER=FORMATTED, and its behavior has been modified for SAS 8. When 

the default ORDER=FORMATTED is in effect for numeric variables for which you have supplied 

no explicit format, the levels are ordered by their internal values. In releases previous to 

SAS 8, numeric class levels with no explicit format were ordered by their BEST12. formatted 

values. In order to revert to the previous method you can specify this format explicitly for 

the CLASS variables. The change was implemented because the former default behavior for 

ORDER=FORMATTED often resulted in levels not being ordered numerically and required 

you to use an explicit format or ORDER=INTERNAL to get the more natural ordering. 

Table 56.4 shows how PROC MIXED interprets values of the ORDER= option. 

Table 56.4 Sort Order and Value of ORDER= Option 

Value of ORDER= Levels Sorted By 

DATA order of appearance in the input data set 

FORMATTED external formatted value, except for numeric variables 

with no explicit format, which are sorted by their unformatted 

(internal) value 

FREQ descending frequency count; levels with the most observations 

come first in the order 

INTERNAL unformatted value 

For FORMATTED and INTERNAL, the sort order is machine dependent. 

For more information about sort order, see the chapter on the SORT procedure in the SAS 

Procedures Guide and the discussion of BY-group processing in SAS Language Reference: 

Concepts.

PLOTS < (global-plot-options ) > < =plot-request < (options ) > > 


PLOTS < (global-plot-options ) > < = (plot-request< (options) >< . . . plot-request< (options) > >) > 

requests that the MIXED procedure produce statistical graphics via the Output Delivery System, 

provided that the ODS GRAPHICS statement has been specified. For general information 

about ODS Graphics, see Chapter 21, “Statistical Graphics Using ODS.” For examples 

of the basic statistical graphics produced by the MIXED procedure and aspects of their computation 

and interpretation, see the section “ODS Graphics” on page 3998. 

The global-plot-options apply to all relevant plots generated by the MIXED procedure. The 

global-plot-options supported by the MIXED procedure follow. 

Global Plot Options 

OBSNO 

uses the data set observation number to identify observations in tooltips, provided that 

the observation number can be determined. Otherwise, the number displayed in tooltips 

is the index of the observation as it is used in the analysis within the BY group. 

ONLY 

suppresses the default plots. Only the plots specifically requested are produced. 

UNPACK 

breaks a graphic that is otherwise paneled into individual component plots. 

ALL 

Specific Plot Options 

The following listing describes the specific plots and their options. 

requests that all plots appropriate for the particular analysis be produced. 

BOXPLOT < (boxplot-options) > 

requests box plots for the effects in your model that consist of classification effects only. 

Note that these effects can involve more than one classification variable (interaction 

and nested effects), but they cannot contain any continuous variables. By default, the 

BOXPLOT request produces box plots based on (conditional) raw residuals for the 

qualifying effects in the MODEL, RANDOM, and REPEATED statements. See the 

discussion of the boxplot-options in a later section for information about how to tune 

your box plot request. 

DISTANCE< (USEINDEX) > 

requests a plot of the likelihood or restricted likelihood distance. When influence diagnostics 

are requested with set selection according to an effect, the USEINDEX option 

enables you to replace the formatted tick values on the horizontal axis with integer indices 

of the effect levels in order to reduce the space taken up by the horizontal plot 

axis.


INFLUENCEESTPLOT< (options) > 

requests panels of the deletiob estimates in an influence analysis, provided that the 

INFLUENCE option is specified in the MODEL statement. No plots are produced for 

fixed-effects parameters associated with singular columns in the X matrix or for covariance 

parameters associated with singularities in the ASYCOV matrix. By default, 

separate panels are produced for the fixed-effects and covariance parameters delete estimates. 

The FIXED and RANDOM options enable you to select these specific panels. 

The UNPACK option produces separate plots for each of the parameter estimates. The 

USEINDEX option replaces formatted tick values for the horizontal axis with integer 

indices. 

INFLUENCESTATPANEL< (options) > 

requests panels of influence statistics. For iterative influence analysis (see the 

INFLUENCE option in the MODEL statement), the panel shows the Cook’s D and 

CovRatio statistics for fixed-effects and covariance parameters, enabling you to gauge 

impact on estimates and precision for both types of estimates. In noniterative analysis, 

only statistics for the fixed effects are plotted. The UNPACK option produces separate 

plots from the elements in the panel. The USEINDEX option replaces formatted tick 

values for the horizontal axis with integer indices. 

RESIDUALPANEL < (residualplot-options) > 

requests a panel of raw residuals. By default, the conditional residuals are produced. 

See the discussion of residualplot-options in a later section for information about how 

to tune this panel. 

STUDENTPANEL < (residualplot-options) > 

requests a panel of studentized residuals. By default, the conditional residuals are produced. 

See the discussion of residualplot-options in a later section for information 

about how to tune this panel. 

PEARSONPANEL < (residualplot-options) > 

requests a panel of Pearson residuals. By default, the conditional residuals are produced. 

See the discussion of residualplot-options in a later section for information 

about how to tune this panel. 

PRESS< (USEINDEX) > 

requests a plot of PRESS residuals or PRESS statistics. These are based on “leave-oneout” 

or “leave-set-out” prediction of the marginal mean. When influence diagnostics 

are requested with set selection according to an effect, the USEINDEX option enables 

you to replace the formatted tick values on the horizontal axis with integer indices of 

the effect levels in order to reduce the space taken up by the horizontal plot axis. 

VCIRYPANEL < (residualplot-options) > 

requests a panel of residual graphics based on the scaled residuals. See the VCIRY 

option in the MODEL statement for details about these scaled residuals. Only the 

UNPACK and BOX options of the residualplot-options are available for this type of 

residual panel. 

NONE 

suppresses all plots.

Residual Plot Options 


The residualplot-options determine both the composition of the panels and the type of 

residuals being plotted. 

BOX 

BOXPLOT 

replaces the inset of summary statistics in the lower-right corner of the panel with 

a box plot of the residual (the “PROC GLIMMIX look”). 

CONDITIONAL 

BLUP 

MARGINAL 

constructs plots from conditional residuals. 

NOBLUP 

constructs plots from marginal residuals. 

UNPACK 

produces separate plots from the elements of the panel. The inset statistics are 

not part of the unpack operation. 

Box Plot Options 

The boxplot-options determine whether box plots are produced for residuals or for 

residuals and observed values, and for which model effects the box plots are constructed. 

The available boxplot-options are as follows. 

CONDITIONAL 

BLUP 

FIXED 

constructs box plots from conditional residuals—that is, residuals using the estimated 

BLUPs of random effects. 

produces box plots for all fixed effects (MODEL statement) consisting entirely 

of classification variables 

GROUP 

produces box plots for all GROUP= effects (RANDOM and REPEATED statement) 

consisting entirely of classification variables 

MARGINAL 

NOBLUP 

constructs box plots from marginal residuals.


NPANEL=number 

provides the ability to break a box plot into multiple graphics. If number is 

negative, no balancing of the number of boxes takes place and number is the 

maximum number of boxes per graphic. If number is positive, the number of 

boxes per graphic is balanced. For example, suppose variable A has 125 levels, 

and consider the following statements: 

ods graphics on; 

proc mixed plots=boxplot(npanel=20); 

class A; 

model y = A; 

run; 

The box balancing results in six plots with 18 boxes each and one plot with 

17 boxes. If number is zero, and this is the default, all levels of the effect are 

displayed in a single plot. 

OBSERVED 

adds box plots of the observed data for the selected effects. 

RANDOM 

produces box plots for all random effects (RANDOM statement) consisting entirely 

of classification variables. This does not include effects specified in the 

GROUP= or SUBJECT= options of the RANDOM statement. 

REPEATED 

produces box plots for the repeated effects (REPEATED statement). This does 

not include effects specified in the GROUP= or SUBJECT= options of the 

REPEATED statement. 

STUDENT 

constructs box plots from studentized residuals rather than from raw residuals. 

SUBJECT 

produces box plots for all SUBJECT= effects (RANDOM and REPEATED statement) 

consisting entirely of classification variables. 

USEINDEX 

uses as the horizontal axis label the index of the effect level rather than the formatted 

value(s). For classification variables with many levels or model effects 

that involve multiple classification variables, the formatted values identifying the 

effect levels can take up too much space as axis tick values, leading to extensive 

thinning. The USEINDEX option replaces tick values constructed from formatted 

values with the internal level number.

RATIO 

Multiple Plot Request 


You can list a plot request one or more times with different options. For example, the following 

statements request a panel of marginal raw residuals, individual plots generated from a 

panel of the conditional raw residuals, and a panel of marginal studentized residuals: 


proc mixed plots(only)=( 

ResidualPanel(marginal) 

ResidualPanel(unpack conditional) 

StudentPanel(marginal box)); 

The inset of residual statistics is replaced in this last panel by a box plot of the studentized 

residuals. Similarly, if you specify the INFLUENCE option in the MODEL statement, then 

the following statements request statistical graphics of fixed-effects deletion estimates (in a 

panel), covariance parameter deletion estimates (unpacked in individual plots), and box plots 

for the SUBJECT= and fixed classification effects based on residuals and observed values: 

ods graphics on / imagefmt=staticmap; 

proc mixed plots(only)=( 

InfluenceEstPlot(fixed) 

InfluenceEstPlot(random unpack) 

BoxPlot(observed fixed subject); 

The STATICMAP image format enables tooltips that show, for example, values of influence 

diagnostics associated with a particular delete estimate. 

This concludes the syntax section for the PLOTS= option in the PROC MIXED statement. 

produces the ratio of the covariance parameter estimates to the estimate of the residual variance 

when the latter exists in the model. 

RIDGE=number 

specifies the starting value for the minimum ridge value used in the Newton-Raphson algorithm. 

The default is 0.3125. 

SCORING< =number > 

requests that Fisher scoring be used in association with the estimation method up to iteration 

number, which is 0 by default. When you use the SCORING= option and PROC MIXED 

converges without stopping the scoring algorithm, PROC MIXED uses the expected Hessian 

matrix to compute approximate standard errors for the covariance parameters instead of 

the observed Hessian. The output from the ASYCOV and ASYCORR options is similarly 

adjusted. 

SIGITER 

is an alias for the NOPROFILE option. 

UPDATE 

is an alias for the LOGNOTE option.


BY Statement 

BY variables ; 

You can specify a BY statement with PROC MIXED to obtain separate analyses on observations in 

groups defined by the BY variables. When a BY statement appears, the procedure expects the input 

data set to be sorted in order of the BY variables. The variables are one or more variables in the 

input data set. 

If your input data set is not sorted in ascending order, use one of the following alternatives: 

Sort the data by using the SORT procedure with a similar BY statement. 

Specify the BY statement options NOTSORTED or DESCENDING in the BY statement for 

the MIXED procedure. The NOTSORTED option does not mean that the data are unsorted 

but rather that the data are arranged in groups (according to values of the BY variables) and 

that these groups are not necessarily in alphabetical or increasing numeric order. 

Create an index on the BY variables by using the DATASETS procedure (in Base SAS software). 

Because sorting the data changes the order in which PROC MIXED reads observations, the sorting 

order for the levels of the CLASS variable might be affected if you have specified ORDER=DATA in 

the PROC MIXED statement. This, in turn, affects specifications in the CONTRAST or ESTIMATE 

statement. 

For more information about the BY statement, see SAS Language Reference: Concepts. For more 

information about the DATASETS procedure, see the Base SAS Procedures Guide. 

CLASS Statement 

CLASS variables ; 

The CLASS statement names the classification variables to be used in the analysis. If the CLASS 

statement is used, it must appear before the MODEL statement. 

Classification variables can be either character or numeric. By default, class levels are determined 

from the entire formatted values of the CLASS variables. Note that this represents a slight change 

from previous releases in the way in which class levels are determined. In releases prior to SAS ® 9, 

class levels were determined by using no more than the first 16 characters of the formatted values. 

If you want to revert to this previous behavior, you can use the TRUNCATE option in the CLASS 

statement. In any case, you can use formats to group values into levels. See the discussion of 

the FORMAT procedure in the Base SAS Procedures Guide and the discussions of the FORMAT 

statement and SAS formats in SAS Language Reference: Dictionary. You can adjust the order of 

CLASS variable levels with the ORDER= option in the PROC MIXED statement. 

You can specify the following option in the CLASS statement after a slash (/):

CONTRAST Statement ✦ 3911 

TRUNCATE 

specifies that class levels should be determined by using no more than the first 16 characters 

of the formatted values of CLASS variables. When formatted values are longer than 16 

characters, you can use this option in order to revert to the levels as determined in releases 

previous to SAS ® 9. 

CONTRAST Statement 

CONTRAST ’label’ < fixed-effect values . . . > 

< | random-effect values . . . >, . . . < / options > ; 

The CONTRAST statement provides a mechanism for obtaining custom hypothesis tests. It is 

patterned after the CONTRAST statement in PROC GLM, although it has been extended to include 

random effects. This enables you to select an appropriate inference space (McLean, Sanders, and 

Stroup 1991). 

You can test the hypothesis L 0 D 0, where L 0 D .K 0 M 0 / and 0 D .ˇ 0 0 /, in several inference 

spaces. The inference space corresponds to the choice of M. When M D 0, your inferences apply 

to the entire population from which the random effects are sampled; this is known as the broad 

inference space. When all elements of M are nonzero, your inferences apply only to the observed 

levels of the random effects. This is known as the narrow inference space, and you can also choose 

it by specifying all of the random effects as fixed. The GLM procedure uses the narrow inference 

space. Finally, by setting to zero the portions of M corresponding to selected main effects and 

interactions, you can choose intermediate inference spaces. The broad inference space is usually 

the most appropriate, and it is used when you do not specify any random effects in the CONTRAST 

statement. 

The CONTRAST statement has the following arguments: 

label identifies the contrast in the table. A label is required for every contrast specified. 

Labels can be up to 200 characters and must be enclosed in quotes. 

fixed-effect identifies an effect that appears in the MODEL statement. The keyword INTER- 

CEPT can be used as an effect when an intercept is fitted in the model. You do 

not need to include all effects that are in the MODEL statement. 

random-effect identifies an effect that appears in the RANDOM statement. The first random 

effect must follow a vertical bar (|); however, random effects do not have to be 

specified. 

values are constants that are elements of the L matrix associated with the fixed and 

random effects. 

The rows of L 0 are specified in order and are separated by commas. The rows of the K 0 component 

of L 0 are specified on the left side of the vertical bars (|). These rows test the fixed effects and are, 

therefore, checked for estimability. The rows of the M 0 component of L 0 are specified on the right 

side of the vertical bars. They test the random effects, and no estimability checking is necessary.


If PROC MIXED finds the fixed-effects portion of the specified contrast to be nonestimable (see the 

SINGULAR= option), then it displays a message in the log. 

The following CONTRAST statement reproduces the F test for the effect A in the split-plot example 

(see Example 56.1): 

contrast ’A broad’ 

A 1 -1 0 A*B .5 .5 -.5 -.5 0 0 , 

A 1 0 -1 A*B .5 .5 0 0 -.5 -.5 / df=6; 

Note that no random effects are specified in the preceding contrast; thus, the inference space is 

broad. The resulting F test has two numerator degrees of freedom because L 0 has two rows. The 

denominator degrees of freedom is, by default, the residual degrees of freedom (9), but the DF= 

option changes the denominator degrees of freedom to 6. 

The following CONTRAST statement reproduces the F test for A when Block and A*Block are considered 

fixed effects (the narrow inference space): 

contrast ’A narrow’ 

A 1 -1 0 

A*B .5 .5 -.5 -.5 0 0 | 

A*Block .25 .25 .25 .25 

-.25 -.25 -.25 -.25 

0 0 0 0 , 

A 1 0 -1 

A*B .5 .5 0 0 -.5 -.5 | 

A*Block .25 .25 .25 .25 

0 0 0 0 

-.25 -.25 -.25 -.25 ; 

The preceding contrast does not contain coefficients for B and Block, because they cancel out in 

estimated differences between levels of A. Coefficients for B and Block are necessary to estimate the 

mean of one of the levels of A in the narrow inference space (see Example 56.1). 

If the elements of L are not specified for an effect that contains a specified effect, then the elements 

of the specified effect are automatically “filled in” over the levels of the higher-order effect. This 

feature is designed to preserve estimability for cases where there are complex higher-order effects. 

The coefficients for the higher-order effect are determined by equitably distributing the coefficients 

of the lower-level effect, as in the construction of least squares means. In addition, if the intercept 

is specified, it is distributed over all classification effects that are not contained by any other 

specified effect. If an effect is not specified and does not contain any specified effects, then all of 

its coefficients in L are set to 0. You can override this behavior by specifying coefficients for the 

higher-order effect. 

If too many values are specified for an effect, the extra ones are ignored; if too few are specified, 

the remaining ones are set to 0. If no random effects are specified, the vertical bar can be omitted; 

otherwise, it must be present. If a SUBJECT effect is used in the RANDOM statement, then the 

coefficients specified for the effects in the RANDOM statement are equitably distributed across the 

levels of the SUBJECT effect. You can use the E option to see exactly which L matrix is used. 

The SUBJECT and GROUP options in the CONTRAST statement are useful for the case when a 

SUBJECT= or GROUP= variable appears in the RANDOM statement, and you want to contrast

CONTRAST Statement ✦ 3913 

different subjects or groups. By default, CONTRAST statement coefficients on random effects are 

distributed equally across subjects and groups. 

PROC MIXED handles missing level combinations of classification variables similarly to the way 

PROC GLM does. Both procedures delete fixed-effects parameters corresponding to missing levels 

in order to preserve estimability. However, PROC MIXED does not delete missing level combinations 

for random-effects parameters because linear combinations of the random-effects parameters 

are always estimable. These conventions can affect the way you specify your CONTRAST coefficients. 

The CONTRAST statement computes the statistic 

F D 

bˇ 

b 

0 

L.L 0bCL/ 1 L 0 

r 

bˇ 

b 

where r D rank.L 0bCL/, and approximates its distribution with an F distribution. In this expression, 

bC is an estimate of the generalized inverse of the coefficient matrix in the mixed model equations. 

See the section “Inference and Test Statistics” on page 3972 for more information about this F 

statistic. 

The numerator degrees of freedom in the F approximation are r D rank.L 0bCL/, and the denominator 

degrees of freedom are taken from the “Tests of Fixed Effects” table and corresponds to the final 

effect you list in the CONTRAST statement. You can change the denominator degrees of freedom 

by using the DF= option. 

You can specify the following options in the CONTRAST statement after a slash (/). 

CHISQ 

requests that chi-square tests be performed in addition to any F tests. A chi-square statistic 

equals its corresponding F statistic times the associate numerator degrees of freedom, and 

the same degrees of freedom are used to compute the p-value for the chi-square test. This 

p-value is always less than that for the F -test, as it effectively corresponds to an F test with 

infinite denominator degrees of freedom. 

DF=number 

specifies the denominator degrees of freedom for the F test. The default is the denominator 

degrees of freedom taken from the “Tests of Fixed Effects” table and corresponds to the final 

effect you list in the CONTRAST statement. 

E 

GROUP coeffs 

requests that the L matrix coefficients for the contrast be displayed. For ODS purposes, the 

label of this “L Matrix Coefficients” table is “Coef.” 

GRP coeffs 

sets up random-effect contrasts between different groups when a GROUP= variable appears in 

the RANDOM statement. By default, CONTRAST statement coefficients on random effects 

are distributed equally across groups.


SINGULAR=number 

tunes the estimability checking. If v is a vector, define ABS(v) to be the absolute value of the 

element of v with the largest absolute value. If ABS(K 0 K 0 T) is greater than C*number for 

any row of K 0 in the contrast, then K is declared nonestimable. Here T is the Hermite form 

matrix .X 0 X/ X 0 X, and C is ABS(K 0 ) except when it equals 0, and then C is 1. The value for 

number must be between 0 and 1; the default is 1E 4. 

SUBJECT coeffs 

SUB coeffs 

sets up random-effect contrasts between different subjects when a SUBJECT= variable appears 

in the RANDOM statement. By default, CONTRAST statement coefficients on random 

effects are distributed equally across subjects. 

ESTIMATE Statement 

ESTIMATE ’label’ < fixed-effect values . . . > 

< | random-effect values . . . >< / options > ; 

The ESTIMATE statement is exactly like a CONTRAST statement, except only one-row L matrices 

are permitted. The actual estimate, L 0 bp, is displayed along with its approximate standard error. An 

approximate t test that L 0 bp = 0 is also produced. 

PROC MIXED selects the degrees of freedom to match those displayed in the “Tests of Fixed 

Effects” table for the final effect you list in the ESTIMATE statement. You can modify the degrees 

of freedom by using the DF= option. 

If PROC MIXED finds the fixed-effects portion of the specified estimate to be nonestimable, then 

it displays “Non-est” for the estimate entries. 

The following examples of ESTIMATE statements compute the mean of the first level of A in the 

split-plot example (see Example 56.1) for various inference spaces: 

estimate ’A1 mean narrow’ intercept 1 

A 1 B .5 .5 A*B .5 .5 | 

block .25 .25 .25 .25 

A*Block .25 .25 .25 .25 

0 0 0 0 

0 0 0 0; 

estimate ’A1 mean intermed’ intercept 1 

A 1 B .5 .5 A*B .5 .5 | 

Block .25 .25 .25 .25; 

estimate ’A1 mean broad’ intercept 1 

A 1 B .5 .5 A*B .5 .5; 

The construction of the L vector for an ESTIMATE statement follows the same rules as listed under 

the CONTRAST statement. 

You can specify the following options in the ESTIMATE statement after a slash (/).

ESTIMATE Statement ✦ 3915 

ALPHA=number 

requests that a t-type confidence interval be constructed with confidence level 1 number. 

The value of number must be between 0 and 1; the default is 0.05. 

CL 

requests that t-type confidence limits be constructed. The confidence level is 0.95 by default; 

this can be changed with the ALPHA= option. 

DF=number 

specifies the degrees of freedom for the t test and confidence limits. The default is the denominator 

degrees of freedom taken from the “Tests of Fixed Effects” table and corresponds 

to the final effect you list in the ESTIMATE statement. 

DIVISOR=number 

specifies a value by which to divide all coefficients so that fractional coefficients can be entered 

as integer numerators. 

E 

GROUP coeffs 

requests that the L matrix coefficients be displayed. For ODS purposes, the name of this “L 

Matrix Coefficients” table is “Coef.” 

GRP coeffs 

sets up random-effect contrasts between different groups when a GROUP= variable appears 

in the RANDOM statement. By default, ESTIMATE statement coefficients on random effects 

are distributed equally across groups. 

LOWER 

LOWERTAILED 

requests that the p-value for the t test be based only on values less than the t statistic. A 

two-tailed test is the default. A lower-tailed confidence limit is also produced if you specify 

the CL option. 


tunes the estimability checking as documented for the SINGULAR= option in the 

CONTRAST statement. 

SUBJECT coeffs 

SUB coeffs 

sets up random-effect contrasts between different subjects when a SUBJECT= variable appears 

in the RANDOM statement. By default, ESTIMATE statement coefficients on random 

effects are distributed equally across subjects. For example, the ESTIMATE statement in the 

following code from Example 56.5 constructs the difference between the random slopes of 

the first two batches. 

proc mixed data=rc; 

class batch; 

model y = month / s; 

random int month / type=un sub=batch s; 

estimate ’slope b1 - slope b2’ | month 1 / subject 1 -1; 

run;


UPPER 

UPPERTAILED 

requests that the p-value for the t test be based only on values greater than the t statistic. A 

two-tailed test is the default. An upper-tailed confidence limit is also produced if you specify 

the CL option. 

ID Statement 

ID variables ; 

The ID statement specifies which variables from the input data set are to be included in the OUTP= 

and OUTPM= data sets from the MODEL statement. If you do not specify an ID statement, then 

all variables are included in these data sets. Otherwise, only the variables you list in the ID statement 

are included. Specifying an ID statement with no variables prevents any variables from being 

included in these data sets. 

LSMEANS Statement 

LSMEANS fixed-effects < / options > ; 

The LSMEANS statement computes least squares means (LS-means) of fixed effects. As in the 

GLM procedure, LS-means are predicted population margins—that is, they estimate the marginal 

means over a balanced population. In a sense, LS-means are to unbalanced designs as class and 

subclass arithmetic means are to balanced designs. The L matrix constructed to compute them is 

the same as the L matrix formed in PROC GLM; however, the standard errors are adjusted for the 

covariance parameters in the model. 

Each LS-mean is computed as Lbˇ, where L is the coefficient matrix associated with the least squares 

mean and bˇ is the estimate of the fixed-effects parameter vector (see the section “Estimating Fixed 

and Random Effects in the Mixed Model” on page 3970). The approximate standard errors for the 

LS-mean is computed as the square root of L.X 0bV 1 X/ L 0 . 

LS-means can be computed for any effect in the MODEL statement that involves CLASS variables. 

You can specify multiple effects in one LSMEANS statement or in multiple LSMEANS statements, 

and all LSMEANS statements must appear after the MODEL statement. As in the ESTIMATE 

statement, the L matrix is tested for estimability, and if this test fails, PROC MIXED displays 

“Non-est” for the LS-means entries. 

Assuming the LS-mean is estimable, PROC MIXED constructs an approximate t test to test the null 

hypothesis that the associated population quantity equals zero. By default, the denominator degrees 

of freedom for this test are the same as those displayed for the effect in the “Tests of Fixed Effects” 

table (see the section “Default Output” on page 3989). 

Table 56.5 summarizes important options in the LSMEANS statement. All LSMEANS options are 

subsequently discussed in alphabetical order.

Table 56.5 Summary of Important LSMEANS Statement Options 


Construction and Computation of LS-Means 

AT modifies covariate value in computing LS-means 

BYLEVEL computes separate margins 

DIFF requests differences of LS-means 

OM specifies weighting scheme for LS-mean computation 

SINGULAR= tunes estimability checking 

SLICE= partitions F tests (simple effects) 

LSMEANS Statement ✦ 3917 

Degrees of Freedom and P-values 

ADJDFE= determines whether to compute row-wise denominator degrees 

of freedom with DDFM=SATTERTHWAITE or 

DDFM=KENWARDROGER 

ADJUST= determines the method for multiple comparison adjustment of LSmean 

differences 

ALPHA=˛ determines the confidence level (1 ˛) 

DF= assigns specific value to degrees of freedom for tests and confidence 

limits 

Statistical Output 

CL constructs confidence limits for means and or mean differences 

CORR displays correlation matrix of LS-means 

COV displays covariance matrix of LS-means 

E prints the L matrix 

You can specify the following options in the LSMEANS statement after a slash (/). 

ADJDFE=SOURCE 

ADJDFE=ROW 

specifies how denominator degrees of freedom are determined when p-values and confidence 

limits are adjusted for multiple comparisons with the ADJUST= option. When you do not 

specify the ADJDFE= option, or when you specify ADJDFE=SOURCE, the denominator 

degrees of freedom for multiplicity-adjusted results are the denominator degrees of freedom 

for the LS-mean effect in the “Type 3 Tests of Fixed Effects” table. When you specify AD- 

JDFE=ROW, the denominator degrees of freedom for multiplicity-adjusted results correspond 

to the degrees of freedom displayed in the DF column of the “Differences of Least Squares 

Means” table. 

The ADJDFE=ROW setting is particularly useful if you want multiplicity adjustments to 

take into account that denominator degrees of freedom are not constant across LS-mean 

differences. This can be the case, for example, when the DDFM=SATTERTHWAITE or 

DDFM=KENWARDROGER degrees-of-freedom method is in effect. 

In one-way models with heterogeneous variance, combining certain ADJUST= options with 

the ADJDFE=ROW option corresponds to particular methods of performing multiplicity adjustments 

in the presence of heteroscedasticity. For example, the following statements fit a


ADJUST=BON 

heteroscedastic one-way model and perform Dunnett’s T3 method (Dunnett 1980), which is 

based on the studentized maximum modulus (ADJUST=SMM): 

proc mixed; 

class A; 

model y = A / ddfm=satterth; 

repeated / group=A; 

lsmeans A / adjust=smm adjdfe=row; 

run; 

If you combine the ADJDFE=ROW option with ADJUST=SIDAK, the multiplicity adjustment 

corresponds to the T2 method of Tamhane (1979), while ADJUST=TUKEY 

corresponds to the method of Games-Howell (Games and Howell 1976). Note that 

ADJUST=TUKEY gives the exact results for the case of fractional degrees of freedom in 

the one-way model, but it does not take into account that the degrees of freedom are subject 

to variability. A more conservative method, such as ADJUST=SMM, might protect the 

overall error rate better. 

Unless the ADJUST= option of the LSMEANS statement is specified, the ADJDFE= option 

has no effect. 

ADJUST=DUNNETT 

ADJUST=SCHEFFE 

ADJUST=SIDAK 

ADJUST=SIMULATE< (sim-options) > 

ADJUST=SMM | GT2 

ADJUST=TUKEY 

requests a multiple comparison adjustment for the p-values and confidence limits for the 

differences of LS-means. By default, PROC MIXED adjusts all pairwise differences unless 

you specify ADJUST=DUNNETT, in which case PROC MIXED analyzes all differences 

with a control level. The ADJUST= option implies the DIFF option. 

The BON (Bonferroni) and SIDAK adjustments involve correction factors described in Chapter 

39, “The GLM Procedure,” and Chapter 58, “The MULTTEST Procedure;” also see Westfall 

and Young (1993) and Westfall et al. (1999). When you specify ADJUST=TUKEY 

and your data are unbalanced, PROC MIXED uses the approximation described in Kramer 

(1956). Similarly, when you specify ADJUST=DUNNETT and the LS-means are correlated, 

PROC MIXED uses the factor-analytic covariance approximation described in Hsu (1992). 

The preceding references also describe the SCHEFFE and SMM adjustments. 

The SIMULATE adjustment computes adjusted p-values and confidence limits from the simulated 

distribution of the maximum or maximum absolute value of a multivariate t random 

vector. All covariance parameters except the residual variance are fixed at their estimated 

values throughout the simulation, potentially resulting in some underdispersion. The simulation 

estimates q, the true .1 ˛/th quantile, where 1 ˛ is the confidence coefficient. The 

default ˛ is 0.05, and you can change this value with the ALPHA= option in the LSMEANS 

statement.


The number of samples is set so that the tail area for the simulated q is within of 1 ˛ with 

100.1 /% confidence. In equation form, 

P.jF .bq/ .1 ˛/j / D 1 

where Oq is the simulated q and F is the true distribution function of the maximum; see 

Edwards and Berry (1987) for details. By default, = 0.005 and = 0.01, placing the tail 

area of Oq within 0.005 of 0.95 with 99% confidence. The ACC= and EPS= sim-options reset 

and , respectively; the NSAMP= sim-option sets the sample size directly; and the SEED= 

sim-option specifies an integer used to start the pseudo-random number generator for the 

simulation. If you do not specify a seed, or if you specify a value less than or equal to zero, 

the seed is generated from reading the time of day from the computer clock. For additional 

descriptions of these and other simulation options, see the section “LSMEANS Statement” 

on page 2456 in Chapter 39, “The GLM Procedure.” 

ALPHA=number 

requests that a t-type confidence interval be constructed for each of the LS-means with confidence 

level 1 number. The value of number must be between 0 and 1; the default is 0.05. 

AT variable = value 

AT (variable-list) = (value-list) 

AT MEANS 

enables you to modify the values of the covariates used in computing LS-means. By default, 

all covariate effects are set equal to their mean values for computation of standard LS-means. 

The AT option enables you to assign arbitrary values to the covariates. Additional columns in 

the output table indicate the values of the covariates. 

If there is an effect containing two or more covariates, the AT option sets the effect equal 

to the product of the individual means rather than the mean of the product (as with standard 

LS-means calculations). The AT MEANS option sets covariates equal to their mean values 

(as with standard LS-means) and incorporates this adjustment to crossproducts of covariates. 

As an example, consider the following invocation of PROC MIXED: 

proc mixed; 

class A; 

model Y = A X1 X2 X1*X2; 

lsmeans A; 

lsmeans A / at means; 

lsmeans A / at X1=1.2; 

lsmeans A / at (X1 X2)=(1.2 0.3); 

run; 

For the first two LSMEANS statements, the LS-means coefficient for X1 is x1 (the mean 

of X1) and for X2 is x2 (the mean of X2). However, for the first LSMEANS statement, the 

coefficient for X1*X2 is x1x2, but for the second LSMEANS statement, the coefficient is 

x1 x2. The third LSMEANS statement sets the coefficient for X1 equal to 1:2 and leaves it 

at x2 for X2, and the final LSMEANS statement sets these values to 1:2 and 0:3, respectively. 

If a WEIGHT variable is present, it is used in processing AT variables. Also, observations 

with missing dependent variables are included in computing the covariate means, unless these


observations form a missing cell and the FULLX option in the MODEL statement is not in 

effect. You can use the E option in conjunction with the AT option to check that the modified 

LS-means coefficients are the ones you want. 

The AT option is disabled if you specify the BYLEVEL option. 

BYLEVEL 

requests PROC MIXED to process the OM data set by each level of the LS-mean effect 

(LSMEANS effect) in question. For more details, see the OM option later in this section. 

CL 

CORR 

COV 

requests that t-type confidence limits be constructed for each of the LS-means. The confidence 

level is 0.95 by default; this can be changed with the ALPHA= option. 

displays the estimated correlation matrix of the least squares means as part of the “Least 

Squares Means” table. 

displays the estimated covariance matrix of the least squares means as part of the “Least 

Squares Means” table. 

DF=number 

specifies the degrees of freedom for the t test and confidence limits. The default is the denominator 

degrees of freedom taken from the “Tests of Fixed Effects” table corresponding to 

the LS-means effect unless the DDFM=SATTERTHWAITE or DDFM=KENWARDROGER 

option is in effect in the MODEL statement. For these DDFM= methods, degrees of freedom 

are determined separately for each test; see the DDFM= option for more information. 

DIFF< =difftype > 

PDIFF< =difftype > 

requests that differences of the LS-means be displayed. The optional difftype specifies which 

differences to produce, with possible values being ALL, CONTROL, CONTROLL, and 

CONTROLU. The difftype ALL requests all pairwise differences, and it is the default. The 

difftype CONTROL requests the differences with a control, which, by default, is the first level 

of each of the specified LSMEANS effects. 

To specify which levels of the effects are the controls, list the quoted formatted values in 

parentheses after the keyword CONTROL. For example, if the effects A, B, and C are classification 

variables, each having two levels, 1 and 2, the following LSMEANS statement 

specifies the (1,2) level of A*B and the (2,1) level of B*C as controls: 

lsmeans A*B B*C / diff=control(’1’ ’2’ ’2’ ’1’); 

For multiple effects, the results depend upon the order of the list, and so you should check the 

output to make sure that the controls are correct. 

Two-tailed tests and confidence limits are associated with the CONTROL difftype. For onetailed 

results, use either the CONTROLL or CONTROLU difftype. The CONTROLL difftype 

tests whether the noncontrol levels are significantly smaller than the control; the upper confidence 

limits for the control minus the noncontrol levels are considered to be infinity and

E 


are displayed as missing. Conversely, the CONTROLU difftype tests whether the noncontrol 

levels are significantly larger than the control; the upper confidence limits for the noncontrol 

levels minus the control are considered to be infinity and are displayed as missing. 

If you want to perform multiple comparison adjustments on the differences of LS-means, you 

must specify the ADJUST= option. 

The differences of the LS-means are displayed in a table titled “Differences of Least Squares 

Means.” For ODS purposes, the table name is “Diffs.” 

requests that the L matrix coefficients for all LSMEANS effects be displayed. For ODS 

purposes, the name of this “L Matrix Coefficients” table is “Coef.” 

OM< =OM-data-set > 

OBSMARGINS< =OM-data-set > 

specifies a potentially different weighting scheme for the computation of LS-means coefficients. 

The standard LS-means have equal coefficients across classification effects; however, 

the OM option changes these coefficients to be proportional to those found in OM-data-set. 

This adjustment is reasonable when you want your inferences to apply to a population that is 

not necessarily balanced but has the margins observed in OM-data-set. 

PDIFF 

By default, OM-data-set is the same as the analysis data set. You can optionally specify another 

data set that describes the population for which you want to make inferences. This data 

set must contain all model variables except for the dependent variable (which is ignored if it 

is present). In addition, the levels of all CLASS variables must be the same as those occurring 

in the analysis data set. Specifying an OM-data-set enables you to construct arbitrarily 

weighted LS-means. 

In computing the observed margins, PROC MIXED uses all observations for which there 

are no missing or invalid independent variables, including those for which there are missing 

dependent variables. Also, if OM-data-set has a WEIGHT variable, PROC MIXED uses 

weighted margins to construct the LS-means coefficients. If OM-data-set is balanced, the 

LS-means are unchanged by the OM option. 

The BYLEVEL option modifies the observed-margins LS-means. Instead of computing the 

margins across all of the OM-data-set, PROC MIXED computes separate margins for each 

level of the LSMEANS effect in question. In this case the resulting LS-means are actually 

equal to raw means for fixed-effects models and certain balanced random-effects models, but 

their estimated standard errors account for the covariance structure that you have specified. If 

the AT option is specified, the BYLEVEL option disables it. 

You can use the E option in conjunction with either the OM or BYLEVEL option to check that 

the modified LS-means coefficients are the ones you want. It is possible that the modified LSmeans 

are not estimable when the standard ones are, or vice versa. Nonestimable LS-means 

are noted as “Non-est” in the output. 

is the same as the DIFF option.



tunes the estimability checking as documented for the SINGULAR= option in the 

CONTRAST statement. 

SLICE= fixed-effect 

SLICE= (fixed-effects) 

specifies effects by which to partition interaction LSMEANS effects. This can produce what 

are known as tests of simple effects (Winer 1971). For example, suppose that A*B is significant, 

and you want to test the effect of A for each level of B. The appropriate LSMEANS 

statement is as follows: 

lsmeans A*B / slice=B; 

This code tests for the simple main effects of A for B, which are calculated by extracting the 

appropriate rows from the coefficient matrix for the A*B LS-means and by using them to form 

an F test. See the section “Inference and Test Statistics” for more information about this F 

test. 

The SLICE option produces a table titled “Tests of Effect Slices.” For ODS purposes, the 

table name is “Slices.” 

MODEL Statement 

MODEL dependent = < fixed-effects >< / options > ; 

The MODEL statement names a single dependent variable and the fixed effects, which determine the 

X matrix of the mixed model (see the section “Parameterization of Mixed Models” on page 3975 

for details). The specification of effects is the same as in the GLM procedure; however, unlike 

PROC GLM, you do not specify random effects in the MODEL statement. The MODEL statement 

is required. 

An intercept is included in the fixed-effects model by default. If no fixed effects are specified, only 

this intercept term is fit. The intercept can be removed by using the NOINT option. 

Table 56.6 summarizes options in the MODEL statement. These are subsequently discussed in 

detail in alphabetical order. 

Table 56.6 Summary of Important MODEL Statement Options 


Model Building 

NOINT excludes fixed-effect intercept from model 

Statistical Computations 

ALPHA=˛ determines the confidence level (1 ˛) for fixed effects 

ALPHAP=˛ determines the confidence level (1 ˛) for predicted values 

CHISQ requests chi-square tests 

DDF= specifies denominator degrees of freedom (list)



MODEL Statement ✦ 3923 

DDFM= specifies the method for computing denominator degrees of freedom 

HTYPE= selects the type of hypothesis test 

INFLUENCE requests influence and case-deletion diagnostics 

NOTEST suppresses hypothesis tests for the fixed effects 

OUTP= specifies output data set for predicted values and related quantities 

OUTPM= specifies output data set for predicted values and related quantities 

RESIDUAL adds Pearson-type and studentized residuals to output data sets 

VCIRY adds scaled marginal residual to output data sets 


CL displays confidence limits for fixed-effects parameter estimates 

CORRB displays correlation matrix of fixed-effects parameter estimates 

COVB displays covariance matrix of fixed-effects parameter estimates 

COVBI displays inverse covariance matrix of fixed-effects parameter estimates 

E, E1, E2, E3 displays L matrix coefficients 

INTERCEPT adds a row for the intercept to test tables 

SOLUTION displays fixed-effects parameter estimates (and scale parameter in 

GLM models) 

Singularity Tolerances 

SINGCHOL= tunes sensitivity in computing Cholesky roots 

SINGRES= tunes singularity criterion for residual variance 

SINGULAR= tunes the sensitivity in sweeping 

ZETA= tunes the sensitivity in forming Type 3 functions 

You can specify the following options in the MODEL statement after a slash (/). 

ALPHA=number 

requests that a t-type confidence interval be constructed for each of the fixed-effects parameters 

with confidence level 1 number. The value of number must be between 0 and 1; the 

default is 0.05. 

ALPHAP=number 

requests that a t-type confidence interval be constructed for the predicted values with confidence 

level 1 number. The value of number must be between 0 and 1; the default is 0.05. 

CHISQ 

CL 

requests that chi-square tests be performed for all specified effects in addition to the F tests. 

Type 3 tests are the default; you can produce the Type 1 and Type 2 tests by using the HTYPE= 

option. 

requests that t-type confidence limits be constructed for each of the fixed-effects parameter


estimates. The confidence level is 0.95 by default; this can be changed with the ALPHA= 

option. 

CONTAIN 

has the same effect as the DDFM=CONTAIN option. 

CORRB 

produces the approximate correlation matrix of the fixed-effects parameter estimates. For 

ODS purposes, the name of this table is “CorrB.” 

COVB 

COVBI 

produces the approximate variance-covariance matrix of the fixed-effects parameter estimates 

bˇ. By default, this matrix equals .X 0bV 1 X/ and results from sweeping .X y/ 0bV 1 .X y/ on 

all but its last pivot and removing the y border. The EMPIRICAL option in the PROC MIXED 

statement changes this matrix into “empirical sandwich” form. For ODS purposes, the name 

of this table is “CovB.” If the degrees-of-freedom method of Kenward and Roger (1997) is in 

effect (DDFM=KENWARDROGER), the COVB matrix changes because the method entails 

an adjustment of the variance-covariance matrix of the fixed effects by the method proposed 

by Prasad and Rao (1990) and Harville and Jeske (1992); see also Kackar and Harville (1984). 

produces the inverse of the approximate variance-covariance matrix of the fixed-effects parameter 

estimates. For ODS purposes, the name of this table is “InvCovB.” 

DDF=value-list 

enables you to specify your own denominator degrees of freedom for the fixed effects. The 

value-list specification is a list of numbers or missing values (.) separated by commas. The 

degrees of freedom should be listed in the order in which the effects appear in the “Tests of 

Fixed Effects” table. If you want to retain the default degrees of freedom for a particular 

effect, use a missing value for its location in the list. For example, the following statement 

assigns 3 denominator degrees of freedom to A and 4.7 to A*B, while those for B remain the 

same: 

model Y = A B A*B / ddf=3,.,4.7; 

If you specify DDFM=SATTERTHWAITE or DDFM=KENWARDROGER, the DDF= option 

has no effect. 

DDFM=CONTAIN 

DDFM=BETWITHIN 

DDFM=RESIDUAL 

DDFM=SATTERTHWAITE 

DDFM=KENWARDROGER< (FIRSTORDER) > 

specifies the method for computing the denominator degrees of freedom for the tests of fixed 

effects resulting from the MODEL, CONTRAST, ESTIMATE, and LSMEANS statements. 

Table 56.7 lists syntax aliases for the degrees-of-freedom methods.

Table 56.7 Aliases for DDFM= Option 

DDFM= Option Alias 

BETWITHIN BW 

CONTAIN CON 

KENWARDROGER KENROG, KR 

RESIDUAL RES 

SATTERTHWAITE SATTERTH, SAT 


The DDFM=CONTAIN option invokes the containment method to compute denominator degrees 

of freedom, and it is the default when you specify a RANDOM statement. The containment 

method is carried out as follows: Denote the fixed effect in question A, and search 

the RANDOM effect list for the effects that syntactically contain A. For example, the random 

effect B(A) contains A, but the random effect C does not, even if it has the same levels as B(A). 

Among the random effects that contain A, compute their rank contribution to the (X Z) matrix. 

The DDF assigned to A is the smallest of these rank contributions. If no effects are found, 

the DDF for A is set equal to the residual degrees of freedom, N rank.X Z/. This choice of 

DDF matches the tests performed for balanced split-plot designs and should be adequate for 

moderately unbalanced designs. 

CAUTION: If you have a Z matrix with a large number of columns, the overall memory 

requirements and the computing time after convergence can be substantial for the containment 

method. If it is too large, you might want to use the DDFM=BETWITHIN option. 

The DDFM=BETWITHIN option is the default for REPEATED statement specifications 

(with no RANDOM statements). It is computed by dividing the residual degrees of freedom 

into between-subject and within-subject portions. PROC MIXED then checks whether 

a fixed effect changes within any subject. If so, it assigns within-subject degrees of freedom 

to the effect; otherwise, it assigns the between-subject degrees of freedom to the effect (see 

Schluchter and Elashoff 1990). If there are multiple within-subject effects containing classification 

variables, the within-subject degrees of freedom are partitioned into components 

corresponding to the subject-by-effect interactions. 

One exception to the preceding method is the case where you have specified no RANDOM 

statements and a REPEATED statement with the TYPE=UN option. In this case, all effects 

are assigned the between-subject degrees of freedom to provide for better small-sample approximations 

to the relevant sampling distributions. DDFM=KENWARDROGER might be a 

better option to try for this case. 

The DDFM=RESIDUAL option performs all tests by using the residual degrees of freedom, 

n rank.X/, where n is the number of observations. 

The DDFM=SATTERTHWAITE option performs a general Satterthwaite approximation for 

the denominator degrees of freedom, computed as follows. Suppose is the vector of unknown 

parameters in V, and suppose C D .X 0 V 1 X/ , where denotes a generalized inverse. 

Let bC and b be the corresponding estimates.


Consider the one-dimensional case, and consider ` to be a vector defining an estimable linear 

combination of ˇ. The Satterthwaite degrees of freedom for the t statistic 

t D `bˇ 

p 

` OC` 0 

is computed as 

D 2.` OC` 0 / 2 

g 0 Ag 

where g is the gradient of `C` 0 with respect to , evaluated at b, and A is the asymptotic 

variance-covariance matrix of b obtained from the second derivative matrix of the likelihood 

equations. 

For the multidimensional case, let L be an estimable contrast matrix and denote the rank of 

LbCL 0 as q > 1. The Satterthwaite denominator degrees of freedom for the F statistic 

F D bˇ 0 L 0 .LbCL 0 / 1 Lbˇ 

q 

are computed by first performing the spectral decomposition LbCL 0 D P 0 DP, where P is an orthogonal 

matrix of eigenvectors and D is a diagonal matrix of eigenvalues, both of dimension 

q q. Define `m to be the mth row of PL, and let 

m D 

2.Dm/ 2 

g 0 m Agm 

where Dm is the mth diagonal element of D and gm is the gradient of `mC` 0 m 

, evaluated at b. Then let 

E D 

qX 

mD1 

m 

m 

2 I. m > 2/ 

where the indicator function eliminates terms for which m 

F are then computed as 

D 2E 

E q 

provided E > q; otherwise is set to zero. 

with respect to 

2. The degrees of freedom for 

This method is a generalization of the techniques described in Giesbrecht and Burns (1985), 

McLean and Sanders (1988), and Fai and Cornelius (1996). The method can also include estimated 

random effects. In this case, append b to bˇ and change bC to be the inverse of the coefficient 

matrix in the mixed model equations. The calculations require extra memory to hold c 

matrices that are the size of the mixed model equations, where c is the number of covariance 

parameters. In the notation of Table 56.25, this is approximately 8q.p C g/.p C g/=2 bytes. 

Extra computing time is also required to process these matrices. The Satterthwaite method 

implemented here is intended to produce an accurate F approximation; however, the results 

can differ from those produced by PROC GLM. Also, the small sample properties of this

E 

E1 

E2 

E3 

FULLX 


approximation have not been extensively investigated for the various models available with 

PROC MIXED. 

The DDFM=KENWARDROGER option performs the degrees of freedom calculations detailed 

by Kenward and Roger (1997). This approximation involves inflating the estimated 

variance-covariance matrix of the fixed and random effects by the method proposed 

by Prasad and Rao (1990) and Harville and Jeske (1992); see also Kackar and Harville 

(1984). Satterthwaite-type degrees of freedom are then computed based on this adjustment. 

By default, the observed information matrix of the covariance parameter estimates 

is used in the calculations. For covariance structures that have nonzero second derivatives 

with respect to the covariance parameters, the Kenward-Roger covariance matrix adjustment 

includes a second-order term. This term can result in standard error shrinkage. 

Also, the resulting adjusted covariance matrix can then be indefinite and is not invariant under 

reparameterization. The FIRSTORDER suboption of the DDFM=KENWARDROGER 

option eliminates the second derivatives from the calculation of the covariance matrix 

adjustment. For the case of scalar estimable functions, the resulting estimator is referred 

to as the Prasad-Rao estimator em @ in Harville and Jeske (1992). The following 

are examples of covariance structures that generally lead to nonzero second derivatives: 

TYPE=ANTE(1), TYPE=AR(1), TYPE=ARH(1), TYPE=ARMA(1,1), TYPE=CSH, 

TYPE=FA, TYPE=FA0(q), TYPE=TOEPH, TYPE=UNR, and all TYPE=SP() structures. 

When the asymptotic variance matrix of the covariance parameters is found to be singular, 

a generalized inverse is used. Covariance parameters with zero variance then do 

not contribute to the degrees-of-freedom adjustment for DDFM=SATTERTHWAITE and 

DDFM=KENWARDROGER, and a message is written to the log. 

This method changes output in the following tables (listed in Table 56.22): Contrast, CorrB, 

CovB, Diffs, Estimates, InvCovB, LSMeans, Slices, SolutionF, SolutionR, Tests1–Tests3. 

The OUTP= and OUTPM= data sets are also affected. 

requests that Type 1, Type 2, and Type 3 L matrix coefficients be displayed for all specified 

effects. For ODS purposes, the name of the table is “Coef.” 

requests that Type 1 L matrix coefficients be displayed for all specified effects. For ODS 

purposes, the name of the table is “Coef.” 





requests that columns of the X matrix that consist entirely of zeros not be eliminated from X; 

otherwise, they are eliminated by default. For a column corresponding to a missing cell to 

be added to X, its particular levels must be present in at least one observation in the analysis


data set along with a missing dependent variable. The use of the FULLX option can affect 

coefficient specifications in the CONTRAST and ESTIMATE statements, as well as covariate 

coefficients from LSMEANS statements specified with the AT MEANS option. 

HTYPE=value-list 

indicates the type of hypothesis test to perform on the fixed effects. Valid entries for value 

are 1, 2, and 3; the default value is 3. You can specify several types by separating the values 

with a comma or a space. The ODS table names are “Tests1” for the Type 1 tests, “Tests2” 

for the Type 2 tests, and “Tests3” for the Type 3 tests. 

INFLUENCE< (influence-options) > 

specifies that influence and case deletion diagnostics are to be computed. 

The INFLUENCE option computes influence diagnostics by noniterative or iterative methods. 

The noniterative diagnostics rely on recomputation formulas under the assumption that 

covariance parameters or their ratios remain fixed. With the possible exception of a profiled 

residual variance, no covariance parameters are updated. This is the default behavior because 

of its computational efficiency. However, the impact of an observation on the overall analysis 

can be underestimated if its effect on covariance parameters is not assessed. Toward this end, 

iterative methods can be applied to gauge the overall impact of observations and to obtain 

influence diagnostics for the covariance parameter estimates. 

If you specify the INFLUENCE option without further suboptions, PROC MIXED computes 

single-case deletion diagnostics and influence statistics for each observation in the data set by 

updating estimates for the fixed-effects parameter estimates, and also the residual variance, if 

it is profiled. The EFFECT=, SELECT=, ITER=, SIZE=, and KEEP= suboptions provide additional 

flexibility in the computation and reporting of influence statistics. Table 56.8 briefly 

describes important suboptions and their effect on the influence analysis. 

Table 56.8 Summary of INFLUENCE Default and Suboptions 

Description Suboption 

Compute influence diagnostics for individual observations default 

Measure influence of sets of observations chosen according to a EFFECT= 

classification variable or effect 

Remove pairs of observations and report the results sorted by de- SIZE=2 

gree of influence 

Remove triples, quadruples of observations, etc. SIZE= 

Allow selection of individual observations, observations sharing SELECT= 

specific levels of effects, and construction of tuples from specified 

subsets of observations 

Update fixed effects and covariance parameters by refitting the ITER=n > 0 

mixed model, adding up to n iterations 

Compute influence diagnostics for the covariance parameters ITER=n > 0 

Update only fixed effects and the residual variance, if it is profiled ITER=0 

Add the reduced-data estimates to the data set created with ODS 

OUTPUT 

ESTIMATES


The modifiers and their default values are discussed in the following paragraphs. The set 

of computed influence diagnostics varies with the suboptions. The most extensive set of 

influence diagnostics is obtained when ITER=n with n > 0. 

You can produce statistical graphics of influence diagnostics when the ODS GRAPHICS 

statement is specified. For general information about ODS Graphics, see Chapter 21, 

“Statistical Graphics Using ODS.” For specific information about the graphics available in 

the MIXED procedure, see the section “ODS Graphics” on page 3998. 

You can specify the following influence-options in parentheses: 

EFFECT=effect 

specifies an effect according to which observations are grouped. Observations sharing 

the same level of the effect are removed from the analysis as a group. The effect must 

contain only classification variables, but they need not be contained in the model. 

ESTIMATES 

EST 

ITER=n 

Removing observations can change the rank of the .X 0 V 1 X/ matrix. This is particularly 

likely to happen when multiple observations are eliminated from the analysis. 

If the rank of the estimated variance-covariance matrix of bˇ changes or its singularity 

pattern is altered, no influence diagnostics are computed. 

specifies that the updated parameter estimates should be written to the ODS output 

data set. The values are not displayed in the “Influence” table, but if you use ODS 

OUTPUT to create a data set from the listing, the estimates are added to the data set. 

If ITER=0, only the fixed-effects estimates are saved. In iterative influence analyses, 

fixed-effects and covariance parameters are stored. The p fixed-effects parameter estimates 

are named Parm1–Parmp, and the q covariance parameter estimates are named 

CovP1–CovPq. The order corresponds to that in the “Solution for Fixed Effects” and 

“Covariance Parameter Estimates” tables. If parameter updates fail—for example, because 

of a loss of rank or a nonpositive definite Hessian—missing values are reported. 

controls the maximum number of additional iterations PROC MIXED performs to update 

the fixed-effects and covariance parameter estimates following data point removal. 

If you specify n > 0, then statistics such as DFFITS, MDFFITS, and the likelihood 

distances measure the impact of observation(s) on all aspects of the analysis. Typically, 

the influence will grow compared to values at ITER=0. In models without RANDOM 

or REPEATED effects, the ITER= option has no effect. 

This documentation refers to analyses when n > 0 simply as iterative influence analysis, 

even if final covariance parameter estimates can be updated in a single step (for 

example, when METHOD=MIVQUE0 or METHOD=TYPE3). This nomenclature reflects 

the fact that only if n > 0 are all model parameters updated, which can require 

additional iterations. If n > 0 and METHOD=REML (default) or METHOD=ML, 

the procedure updates fixed effects and variance-covariance parameters after removing 

the selected observations with additional Newton-Raphson iterations, starting from the 

converged estimates for the entire data. The process stops for each observation or set of 

observations if the convergence criterion is satisfied or the number of further iterations


exceeds n. If n > 0 and METHOD=TYPE1, TYPE2, or TYPE3, ANOVA estimates of 

the covariance parameters are recomputed in a single step. 

Compared to noniterative updates, the computations are more involved. In particular 

for large data sets and/or a large number of random effects, iterative updates require 

considerably more resources. A one-step (ITER=1) or two-step update might be a 

good compromise. The output includes the number of iterations performed, which is 

less than n if the iteration converges. If the process does not converge in n iterations, 

you should be careful in interpreting the results, especially if n is fairly large. 

Bounds and other restrictions on the covariance parameters carry over from the fulldata 

model. Covariance parameters that are not iterated in the model fit to the full 

data (the NOITER or HOLD option in the PARMS statement) are likewise not updated 

in the refit. In certain models, such as random-effects models, the ratios between the 

covariance parameters and the residual variance are maintained rather than the actual 

value of the covariance parameter estimate (see the section “Influence Diagnostics” on 

page 3982). 

KEEP=n 

determines how many observations are retained for display and in the output data set or 

how many tuples if you specify SIZE=. The output is sorted by an influence statistic as 

discussed for the SIZE= suboption. 

SELECT=value-list 

specifies which observations or effect levels are chosen for influence calculations. If 

the SELECT= suboption is not specified, diagnostics are computed as follows: 

for all observations, if EFFECT= or SIZE= are not given 

for all levels of the specified effect, if EFFECT= is specified 

for all tuples of size k formed from the observations in value-list, if SIZE=k is 

specified 

When you specify an effect with the EFFECT= option, the values in value-list represent 

indices of the levels in the order in which PROC MIXED builds classification effects. 

Which observations in the data set correspond to this index depends on the order of the 

variables in the CLASS statement, not the order in which the variables appear in the 

interaction effect. See the section “Parameterization of Mixed Models” on page 3975 

to understand precisely how the procedure indexes nested and crossed effects and how 

levels of classification variables are ordered. The actual values of the classification 

variables involved in the effect are shown in the output so you can determine which 

observations were removed. 

If the EFFECT= suboption is not specified, the SELECT= value list refers to the sequence 

in which observations are read from the input data set or from the current BY 

group if there is a BY statement. This indexing is not necessarily the same as the observation 

numbers in the input data set, for example, if a WHERE clause is specified or 

during BY processing.

SIZE=n 


instructs PROC MIXED to remove groups of observations formed as tuples of size 

n. For example, SIZE=2 specifies all n .n 1/=2 unique pairs of observations. 

The number of tuples for SIZE=k is nŠ=.kŠ.n k/Š/ and grows quickly with n and 

k. Using the SIZE= option can result in considerable computing time. The MIXED 

procedure displays by default only the 50 tuples with the greatest influence. Use the 

KEEP= option to override this default and to retain a different number of tuples in the 

listing or ODS output data set. Regardless of the KEEP= specification, all tuples are 

evaluated and the results are ordered according to an influence statistic. This statistic 

is the (restricted) likelihood distance as a measure of overall influence if ITER= n > 0 

or when a residual variance is profiled. When likelihood distances are unavailable, the 

results are ordered by the PRESS statistic. 

To reduce computational burden, the SIZE= option can be combined with the 

SELECT=value-list modifier. For example, the following statements evaluate all 

15 D 6 5=2 pairs formed from observations 13, 14, 18, 30, 31, and 33 and display 

the five pairs with the greatest influence: 

proc mixed; 

class a m f; 

model penetration = a m / 

influence(size=2 keep=5 

select=13,14,18,30,31,33); 

random f(m); 

run; 

If any observation in a tuple contains missing values or has otherwise not contributed to 

the analysis, the tuple is not evaluated. This guarantees that the displayed results refer 

to the same number of observations, so that meaningful statistics are available by which 

to order the results. If computations fail for a particular tuple—for example, because 

the .X 0 V 1 X/ matrix changes rank or the G matrix is not positive definite—no results 

are produced. Results are retained when the maximum number of iterative updates is 

exceeded in iterative influence analyses. 

The SIZE= suboption cannot be combined with the EFFECT= suboption. As in the 

case of the EFFECT= suboption, the statistics being computed are those appropriate 

for removal of multiple data points, even if SIZE=1. 

For ODS purposes the name of the “Influence Diagnostics” table is “Influence.” The variables 

in this table depend on whether you specify the EFFECT=, SIZE=, or KEEP= suboption and 

whether covariance parameters are iteratively updated. When ITER=0 (the default), certain 

influence diagnostics are meaningful only if the residual variance is profiled. Table 56.9 and 

Table 56.10 summarize the statistics obtained depending on the model and modifiers. The 

last column in these tables gives the variable name in the ODS OUTPUT INFLUENCE= 

data set. Restricted likelihood distances are reported instead of the likelihood distance unless 

METHOD=ML. See the section “Influence Diagnostics” on page 3982 for details about the 

individual statistics.


Table 56.9 Statistics Computed with INFLUENCE Option, Noniterative Analysis (ITER=0) 

Suboption 2 Statistic Variable 

Profiled Name 

Default Yes Observed value Observed 

Predicted value Predicted 

Residual Residual 

Leverage Leverage 

PRESS residual PRESSRes 

Internally studentized residual Student 

Externally studentized residual RStudent 

RMSE without deleted obs RMSE 

Cook’s D CookD 

DFFITS DFFITS 

CovRatio COVRATIO 

(Restricted) likelihood distance RLD, LD 

Default No Observed value Observed 



Leverage Leverage 

PRESS residual PRESSRes 



EFFECT=, Yes Observations in level (tuple) Nobs 

SIZE=, PRESS statistic PRESS 

or KEEP= Cook’s D CookD 

MDFFITS MDFFITS 


COVTRACE COVTRACE 

RMSE without deleted level (tuple) RMSE 


EFFECT=, No Observations in level (tuple) Nobs 

SIZE=, PRESS statistic PRESS 

or KEEP= Cook’s D CookD 

Table 56.10 Statistics Computed with INFLUENCE Option, Iterative Analysis (ITER=n > 0) 

Suboption Statistic Variable 

Name 

Default Number of iterations Iter 

Observed value Observed 



Leverage Leverage


Suboption Statistic Variable 

Name 


PRESS residual PRESSres 


Externally studentized residual RStudent 

RMSE without deleted obs (if possible) RMSE 


DFFITS DFFITS 


Cook’s D CovParms CookDCP 

CovRatio CovParms COVRATIOCP 

MDFFITS CovParms MDFFITSCP 


EFFECT=, Observations in level (tuple) Nobs 

SIZE=, Number of iterations Iter 

or KEEP= PRESS statistic PRESS 

RMSE without deleted level (tuple) RMSE 


MDFFITS MDFFITS 


COVTRACE COVTRACE 

Cook’s D CovParms CookDCP 

CovRatio CovParms COVRATIOCP 

MDFFITS CovParms MDFFITSCP 


INTERCEPT 

adds a row to the tables for Type 1, 2, and 3 tests corresponding to the overall intercept. 

LCOMPONENTS 

requests an estimate for each row of the L matrix used to form tests of fixed effects. Components 

corresponding to Type 3 tests are the default; you can produce the Type 1 and Type 2 

component estimates with the HTYPE= option. 

Tests of fixed effects involve testing of linear hypotheses of the form Lˇ D 0. The matrix 

L is constructed from Type 1, 2, or 3 estimable functions. By default the MIXED procedure 

constructs Type 3 tests. In many situations, the individual rows of the matrix L represent 

contrasts of interest. For example, in a one-way classification model, the Type 3 estimable 

functions define differences of factor-level means. In a balanced two-way layout, the rows of 

L correspond to differences of cell means. 

For example, suppose factors A and B have a and b levels, respectively. The following statements 

produce .a 1/ one degree of freedom tests for the rows of L associated with the Type 

1 and Type 3 estimable functions for factor A, .b 1/ tests for the rows of L associated with 

factor B, and a single test for the Type 1 and Type 3 coefficients associated with regressor X:


class A B; 

model y = A B x / htype=1,3 lcomponents; 

The denominator degrees of freedom associated with a row of L are the same as those in the 

corresponding “Tests of Fixed Effects” table, except for DDFM=KENWARDROGER and 

DDFM=SATTERTHWAITE. For these degree of freedom methods, the denominator degrees 

of freedom are computed separately for each row of L. 

For ODS purposes, the name of the table containing all requested component tests is “LComponents.” 

See Example 56.9 for applications of the LCOMPONENTS option. 

NOCONTAIN 

has the same effect as the DDFM=RESIDUAL option. 

NOINT 

requests that no intercept be included in the model. An intercept is included by default. 

NOTEST 

specifies that no hypothesis tests be performed for the fixed effects. 

OUTP=SAS-data-set 

OUTPRED=SAS-data-set 

specifies an output data set containing predicted values and related quantities. This option 

replaces the P option from SAS 6. 

Predicted values are formed by using the rows from (X Z) as L matrices. Thus, predicted 

values from the original data are Xbˇ C Zb. Their approximate standard errors of prediction 

are formed from the quadratic form of L with bC defined in the section “Statistical Properties” 

on page 3971. The L95 and U95 variables provide a t-type confidence interval for the 

predicted values, and they correspond to the L95M and U95M variables from the GLM and 

REG procedures for fixed-effects models. The residuals are the observed minus the predicted 

values. Predicted values for data points other than those observed can be obtained by using 

missing dependent variables in your input data set. 

Specifications that have a REPEATED statement with the SUBJECT= option and missing dependent 

variables compute predicted values by using empirical best linear unbiased prediction 

(EBLUP). Using hats . O / to denote estimates, the EBLUP formula is 

Om D Xm Ǒ C OCm OV 1 .y X Ǒ/ 

where m represents a hypothetical realization of a missing data vector with associated design 

matrix Xm. The matrix Cm is the model-based covariance matrix between m and the observed 

data y, and other notation is as presented in the section “Mixed Models Theory” on page 3962. 

The estimated prediction variance is as follows: 

cVar. Om m/ D OVm OCm OV 1 OC T m C 

ŒXm OCm OV 1 X.X T OV 1 X/ ŒXm OCm OV 1 X T


where Vm is the model-based variance matrix of m. For further details, see Henderson (1984) 

and Harville (1990). This feature can be useful for forecasting time series or for computing 

spatial predictions. 

By default, all variables from the input data set are included in the OUTP= data set. You can 

select a subset of these variables by using the ID statement. 

OUTPM=SAS-data-set 

OUTPREDM=SAS-data-set 

specifies an output data set containing predicted means and related quantities. This option 

replaces the PM option from SAS 6. 

The output data set is of the same form as that resulting from the OUTP= option, except 

that the predicted values do not incorporate the EBLUP values Zb. They also do not use the 

EBLUPs for specifications that have a REPEATED statement with the SUBJECT= option and 

missing dependent variables. The predicted values are formed as Xbˇ in the OUTPM= data 

set, and standard errors are quadratic forms in the approximate variance-covariance matrix of 

bˇ as displayed by the COVB option. 

By default, all variables from the input data set are included in the OUTPM= data set. You 

can select a subset of these variables by using the ID statement. 

RESIDUAL 

requests that Pearson-type and (internally) studentized residuals be added to the OUTP= and 

OUTPM= data sets. Studentized residuals are raw residuals standardized by their estimated 

standard error. When residuals are internally studentized, the data point in question has 

contributed to the estimation of the covariance parameter estimates on which the standard 

error of the residual is based. Externally studentized residuals can be computed with the 

INFLUENCE option. Pearson-type residuals scale the residual by the standard deviation of 

the response. 

The option has no effect unless the OUTP= or OUTPM= option is specified or unless you request 

statistical graphics with the ODS GRAPHICS statement. For general information about 

ODS Graphics, see Chapter 21, “Statistical Graphics Using ODS.” For specific information 

about the graphics available in the MIXED procedure, see the section “ODS Graphics” on 

page 3998. For computational details about studentized and Pearson residuals in MIXED, 

see the section “Residual Diagnostics” on page 3980. 

SINGCHOL=number 

tunes the sensitivity in computing Cholesky roots. If a diagonal pivot element is less than 

D*number as PROC MIXED performs the Cholesky decomposition on a matrix, the associated 

column is declared to be linearly dependent upon previous columns and is set to 0. The 

value D is the original diagonal element of the matrix. The default for number is 1E4 times 

the machine epsilon; this product is approximately 1E 12 on most computers. 

SINGRES=number 

sets the tolerance for which the residual variance is considered to be zero. The default is 1E4 

times the machine epsilon; this product is approximately 1E 12 on most computers.



tunes the sensitivity in sweeping. If a diagonal pivot element is less than D*number as 

PROC MIXED sweeps a matrix, the associated column is declared to be linearly dependent 

upon previous columns, and the associated parameter is set to 0. The value D is the original 

diagonal element of the matrix. The default is 1E4 times the machine epsilon; this product is 

approximately 1E 12 on most computers. 

SOLUTION 

S 

VCIRY 

XPVIX 

XPVIXI 

requests that a solution for the fixed-effects parameters be produced. Using notation from 

the section “Mixed Models Theory” on page 3962, the fixed-effects parameter estimates 

are bˇ and their approximate standard errors are the square roots of the diagonal elements 

of .X 0bV 1 X/ . You can output this approximate variance matrix with the COVB option 

or modify it with the EMPIRICAL option in the PROC MIXED statement or the 

DDFM=KENWARDROGER option in the MODEL statement. 

Along with the estimates and their approximate standard errors, a t statistic is computed as the 

estimate divided by its standard error. The degrees of freedom for this t statistic matches the 

one appearing in the “Tests of Fixed Effects” table under the effect containing the parameter. 

The “Pr > |t|” column contains the two-tailed p-value corresponding to the t statistic and 

associated degrees of freedom. You can use the CL option to request confidence intervals 

for all of the parameters; they are constructed around the estimate by using a radius of the 

standard error times a percentage point from the t distribution. 

requests that responses and marginal residuals be scaled by the inverse Cholesky root of the 

marginal variance-covariance matrix. The variables ScaledDep and ScaledResid are added to 

the OUTPM= data set. These quantities can be important in bootstrapping of data or residuals. 

Examination of the scaled residuals is also helpful in diagnosing departures from normality. 

Notice that the results of this scaling operation can depend on the order in which the MIXED 

procedure processes the data. 

The VCIRY option has no effect unless you also use the OUTPM= option or unless you request 

statistical graphics with the ODS GRAPHICS statement. For general information about 

ODS Graphics, see Chapter 21, “Statistical Graphics Using ODS.” For specific information 

about the graphics available in the MIXED procedure, see the section “ODS Graphics” on 

page 3998. 

is an alias for the COVBI option. 

is an alias for the COVB option. 

ZETA=number 

tunes the sensitivity in forming Type 3 functions. Any element in the estimable function basis 

with an absolute value less than number is set to 0. The default is 1E 8.

PARMS Statement 

PARMS (value-list) . . . < / options > ; 

PARMS Statement ✦ 3937 

The PARMS statement specifies initial values for the covariance parameters, or it requests a grid 

search over several values of these parameters. You must specify the values in the order in which 

they appear in the “Covariance Parameter Estimates” table. 

The value-list specification can take any of several forms: 

m a single value 

m1; m2; : : : ; mn several values 

m to n a sequence where m equals the starting value, n equals the ending value, and the 

increment equals 1 

m to n by i a sequence where m equals the starting value, n equals the ending value, and the 

increment equals i 

m1; m2 to m3 

mixed values and sequences 

You can use the PARMS statement to input known parameters. Referring to the split-plot example 

(Example 56.1), suppose the three variance components are known to be 60, 20, and 6. The SAS 

statements to fix the variance components at these values are as follows: 

proc mixed data=sp noprofile; 

class Block A B; 

model Y = A B A*B; 

random Block A*Block; 

parms (60) (20) (6) / noiter; 

run; 

The NOPROFILE option requests PROC MIXED to refrain from profiling the residual variance parameter 

during its calculations, thereby enabling its value to be held at 6 as specified in the PARMS 

statement. The NOITER option prevents any Newton-Raphson iterations so that the subsequent 

results are based on the given variance components. You can also specify known parameters of G 

by using the GDATA= option in the RANDOM statement. 

If you specify more than one set of initial values, PROC MIXED performs a grid search of the 

likelihood surface and uses the best point on the grid for subsequent analysis. Specifying a large 

number of grid points can result in long computing times. The grid search feature is also useful for 

exploring the likelihood surface. (See Example 56.3.) 

The results from the PARMS statement are the values of the parameters on the specified grid (denoted 

by CovP1–CovPn), the residual variance (possibly estimated) for models with a residual 

variance parameter, and various functions of the likelihood. 

For ODS purposes, the name of the “Parameter Search” table is “ParmSearch.” 

You can specify the following options in the PARMS statement after a slash (/).


HOLD=value-list 

EQCONS=value-list 

specifies which parameter values PROC MIXED should hold to equal the specified values. 

For example, the following statement constrains the first and third covariance parameters to 

equal 5 and 2, respectively: 

parms (5) (3) (2) (3) / hold=1,3; 

LOGDETH 

evaluates the log determinant of the Hessian matrix for each point specified in the PARMS 

statement. A Log Det H column is added to the “Parameter Search” table. 

LOWERB=value-list 

enables you to specify lower boundary constraints on the covariance parameters. The valuelist 

specification is a list of numbers or missing values (.) separated by commas. You must 

list the numbers in the order that PROC MIXED uses for the covariance parameters, and 

each number corresponds to the lower boundary constraint. A missing value instructs PROC 

MIXED to use its default constraint, and if you do not specify numbers for all of the covariance 

parameters, PROC MIXED assumes the remaining ones are missing. 

An example for which this option is useful is when you want to constrain the G matrix to 

be positive definite in order to avoid the more computationally intensive algorithms required 

when G becomes singular. The corresponding statements for a random coefficients model are 

as follows: 

proc mixed; 

class person; 

model y = time; 

random int time / type=fa0(2) sub=person; 

parms / lowerb=1e-4,.,1e-4; 

run; 

Here the TYPE=FA0(2) structure is used in order to specify a Cholesky root parameterization 

for the 2 2 unstructured blocks in G. This parameterization ensures that the G matrix is 

nonnegative definite, and the PARMS statement then ensures that it is positive definite by 

constraining the two diagonal terms to be greater than or equal to 1E 4. 

NOBOUND 

requests the removal of boundary constraints on covariance parameters. For example, variance 

components have a default lower boundary constraint of 0, and the NOBOUND option 

allows their estimates to be negative. 

NOITER 

requests that no Newton-Raphson iterations be performed and that PROC MIXED use the 

best value from the grid search to perform inferences. By default, iterations begin at the best 

value from the PARMS grid search. 

NOPROFILE 

specifies a different computational method for the residual variance during the grid search. 

By default, PROC MIXED estimates this parameter by using the profile likelihood when

OLS 

PRIOR Statement ✦ 3939 

appropriate. This estimate is displayed in the Variance column of the “Parameter Search” 

table. The NOPROFILE option suppresses the profiling and uses the actual value of the 

specified variance in the likelihood calculations. 

requests starting values corresponding to the usual general linear model. Specifically, all 

variances and covariances are set to zero except for the residual variance, which is set equal 

to its ordinary least squares (OLS) estimate. This option is useful when the default MIVQUE0 

procedure produces poor starting values for the optimization process. 

PARMSDATA=SAS-data-set 

PDATA=SAS-data-set 

reads in covariance parameter values from a SAS data set. The data set should contain the Est 

or Covp1–Covpn variables. 

RATIOS 

indicates that ratios with the residual variance are specified instead of the covariance parameters 

themselves. The default is to use the individual covariance parameters. 

UPPERB=value-list 

enables you to specify upper boundary constraints on the covariance parameters. The valuelist 

specification is a list of numbers or missing values (.) separated by commas. You must 

list the numbers in the order that PROC MIXED uses for the covariance parameters, and 

each number corresponds to the upper boundary constraint. A missing value instructs PROC 

MIXED to use its default constraint, and if you do not specify numbers for all of the covariance 

parameters, PROC MIXED assumes that the remaining ones are missing. 

PRIOR Statement 

PRIOR < distribution >< / options > ; 

The PRIOR statement enables you to carry out a sampling-based Bayesian analysis in PROC 

MIXED. It currently operates only with variance component models. The analysis produces a 

SAS data set containing a pseudo-random sample from the joint posterior density of the variance 

components and other parameters in the mixed model. 

The posterior analysis is performed after all other PROC MIXED computations. It begins with the 

“Posterior Sampling Information” table, which provides basic information about the posterior sampling 

analysis, including the prior densities, sampling algorithm, sample size, and random number 

seed. For ODS purposes, the name of this table is “Posterior.” 

By default, PROC MIXED uses an independence chain algorithm in order to generate the posterior 

sample (Tierney 1994). This algorithm works by generating a pseudo-random proposal from a 

convenient base distribution, chosen to be as close as possible to the posterior. The proposal is then 

retained in the sample with probability proportional to the ratio of weights constructed by taking 

the ratio of the true posterior to the base density. If a proposal is not accepted, then a duplicate of 

the previous observation is added to the chain.


In selecting the base distribution, PROC MIXED makes use of the fact that the fixed-effects parameters 

can be analytically integrated out of the joint posterior, leaving the marginal posterior density 

of the variance components. In order to better approximate the marginal posterior density of the 

variance components, PROC MIXED transforms them by using the MIVQUE(0) equations. You 

can display the selected transformation with the PTRANS option or specify your own with the 

TDATA= option. The density of the transformed parameters is then approximated by a product of 

inverted gamma densities (see Gelfand et al. 1990). 

To determine the parameters for the inverted gamma densities, PROC MIXED evaluates the logarithm 

of the posterior density over a grid of points in each of the transformed parameters, and you 

can display the results of this search with the PSEARCH option. PROC MIXED then performs a 

linear regression of these values on the logarithm of the inverted gamma density. The resulting base 

densities are displayed in the “Base Densities” table; for ODS purposes, the name of this table is 

“BaseDen.” You can input different base densities with the BDATA= option. 

At the end of the sampling, the “Acceptance Rates” table displays the acceptance rate computed 

as the number of accepted samples divided by the total number of samples generated. For ODS 

purposes, the name of the “Acceptance Rates” table is “AcceptanceRates.” 

The OUT= option specifies the output data set containing the posterior sample. PROC MIXED automatically 

includes all variance component parameters in this data set (labeled COVP1–COVPn), 

the Type 3 F statistics constructed as in Ghosh (1992) discussing Schervish (1992) (labeled T3Fn), 

the log values of the posterior (labeled LOGF), the log of the base sampling density (labeled 

LOGG), and the log of their ratio (labeled LOGRATIO). If you specify the SOLUTION option 

in the MODEL statement, the data set also contains a random sample from the posterior density 

of the fixed-effects parameters (labeled BETAn); and if you specify the SOLUTION option in 

the RANDOM statement, the table contains a random sample from the posterior density of the 

random-effects parameters (labeled GAMn). PROC MIXED also generates additional variables 

corresponding to any CONTRAST, ESTIMATE, or LSMEANS statement that you specify. 

Subsequently, you can use SAS/INSIGHT or the UNIVARIATE, CAPABILITY, or KDE procedure 

to analyze the posterior sample. 

The prior density of the variance components is, by default, a noninformative version of Jeffreys’ 

prior (Box and Tiao 1973). You can also specify informative priors with the DATA= option or a 

flat (equal to 1) prior for the variance components. The prior density of the fixed-effects parameters 

is assumed to be flat (equal to 1), and the resulting posterior is conditionally multivariate normal 

(conditioning on the variance component parameters) with mean .X 0 V 1 X/ X 0 V 1 y and variance 

.X 0 V 1 X/ . 

The distribution argument in the PRIOR statement determines the prior density for the variance 

component parameters of your mixed model. Valid values are as follows. 

DATA= 

enables you to input the prior densities of the variance components used by the sampling 

algorithm. This data set must contain the Type and Parm1–Parmn variables, where n is the 

largest number of parameters among each of the base densities. The format of the DATA= 

data set matches that created by PROC MIXED in the “Base Densities” table, so you can 

output the densities from one run and use them as input for a subsequent run.

PRIOR Statement ✦ 3941 

JEFFREYS 

specifies a noninformative reference version of Jeffreys’ prior constructed by using the square 

root of the determinant of the expected information matrix as in (1.3.92) of Box and Tiao 

(1973). This is the default prior. 

FLAT 

specifies a prior density equal to 1 everywhere, making the likelihood function the posterior. 

You can specify the following options in the PRIOR statement after a slash (/). 

ALG=IC | INDCHAIN 

ALG=IS | IMPSAMP 

ALG=RS | REJSAMP 

ALG=RWC | RWCHAIN 

specifies the algorithm used for generating the posterior sample. The ALG=IC option requests 

an independence chain algorithm, and it is the default. The option ALG=IS requests 

importance sampling, ALG=RS requests rejection sampling, and ALG=RWC requests a random 

walk chain. For more information about these techniques, see Ripley (1987), Smith and 

Gelfand (1992), and Tierney (1994). 

BDATA= 

enables you to input the base densities used by the sampling algorithm. This data set must 

contain the Type and Parm1–Parmn variables, where n is the largest number of parameters 

among each of the base densities. The format of the BDATA= data set matches that created 

by PROC MIXED in the “Base Densities” table, so you can output the densities from one run 

and use them as input for a subsequent run. 

GRID=(value-list) 

specifies a grid of values over which to evaluate the posterior density. The value-list syntax is 

the same as in the PARMS statement, and you must specify an output data set name with the 

OUTG= option. 

GRIDT=(value-list) 

specifies a transformed grid of values over which to evaluate the posterior density. The valuelist 

syntax is the same as in the PARMS statement, and you must specify an output data set 

name with the OUTGT= option. 

IFACTOR=number 

is an alias for the SFACTOR= option. 

LOGNOTE=number 

instructs PROC MIXED to write a note to the SAS log after it generates the sample corresponding 

to each multiple of number. This is useful for monitoring the progress of CPUintensive 

runs. 

LOGRBOUND=number 

specifies the bounding constant for rejection sampling. The value of number equals the maximum 

of logff =gg over the variance component parameter space, where f is the posterior 

density and g is the product inverted gamma densities used to perform rejection sampling. 

When performing the rejection sampling, you might encounter the following message:


WARNING: The log ratio bound of LL was violated at sample XX. 

When this occurs, PROC MIXED reruns an optimization algorithm to determine a new log 

upper bound and then restarts the rejection sampling. The resulting OUT= data set contains 

all observations that have been generated; therefore, assuming that you have requested N 

samples, you should retain only the final N observations in this data set for analysis purposes. 

NSAMPLE=number 

specifies the number of posterior samples to generate. The default is 1000, but more accurate 

results are obtained with larger samples such as 10000. 

NSEARCH=number 

specifies the number of posterior evaluations PROC MIXED makes for each transformed 

parameter in determining the parameters for the inverted gamma densities. The default is 20. 

OUT=SAS-data-set 

creates an output data set containing the sample from the posterior density. 

OUTG=SAS-data-set 

creates an output data set from the grid evaluations specified in the GRID= option. 

OUTGT=SAS-data-set 

creates an output data set from the transformed grid evaluations specified in the GRIDT= 

option. 

PSEARCH 

displays the search used to determine the parameters for the inverted gamma densities. For 

ODS purposes, the name of the table is “Search.” 

PTRANS 

displays the transformation of the variance components. For ODS purposes, the name of the 

table is “Trans.” 

SEED=number 

specifies an integer used to start the pseudo-random number generator for the simulation. If 

you do not specify a seed, or if you specify a value less than or equal to zero, the seed is by 

default generated from reading the time of day from the computer clock. You should use a 

positive seed (less than 2 31 1) whenever you want to duplicate the sample in another run of 

PROC MIXED. 

SFACTOR=number 

enables you to adjust the range over which PROC MIXED searches the transformed parameters 

in order to determine the parameters for the inverted gamma densities. PROC MIXED 

determines the range by first transforming the estimates from the standard PROC MIXED 

analysis (REML, ML, or MIVQUE0, depending upon which estimation method you select). 

It then multiplies and divides the transformed estimates by 2 number to obtain upper and 

lower bounds, respectively. Transformed values that produce negative variance components 

in the original scale are not included in the search. The default value is 1; number must be 

greater than 0.5.

RANDOM Statement ✦ 3943 

TDATA= 

enables you to input the transformation of the covariance parameters used by the sampling 

algorithm. This data set should contain the CovP1–CovPn variables. The format of the 

TDATA= data set matches that created by PROC MIXED in the “Trans” table, so you can 

output the transformation from one run and use it as input for a subsequent run. 

TRANS=EXPECTED 

TRANS=MIVQUE0 

TRANS=OBSERVED 

specifies the particular algorithm used to determine the transformation of the covariance parameters. 

The default is MIVQUE0, indicating a transformation based on the MIVQUE(0) 

equations. The other two options indicate the type of Hessian matrix used in constructing the 

transformation via a Cholesky root. 

UPDATE=number 

is an alias for the LOGNOTE= option. 

RANDOM Statement 

RANDOM random-effects < / options > ; 

The RANDOM statement defines the random effects constituting the vector in the mixed model. 

It can be used to specify traditional variance component models (as in the VARCOMP procedure) 

and to specify random coefficients. The random effects can be classification or continuous, and 

multiple RANDOM statements are possible. 

Using notation from the section “Mixed Models Theory” on page 3962, the purpose of the RAN- 

DOM statement is to define the Z matrix of the mixed model, the random effects in the vector, 

and the structure of G. The Z matrix is constructed exactly as the X matrix for the fixed effects, 

and the G matrix is constructed to correspond with the effects constituting Z. The structure of G is 

defined by using the TYPE= option. 

You can specify INTERCEPT (or INT) as a random effect to indicate the intercept. PROC MIXED 

does not include the intercept in the RANDOM statement by default as it does in the MODEL 

statement. 

Table 56.11 summarizes important options in the RANDOM statement. All options are subsequently 

discussed in alphabetical order. 

Table 56.11 Summary of Important RANDOM Statement Options 


Construction of Covariance Structure 

GDATA= requests that the G matrix be read from a SAS data set 

GROUP= varies covariance parameters by groups 

LDATA= specifies data set with coefficient matrices for TYPE= LIN 

NOFULLZ eliminates columns in Z corresponding to missing values




RATIOS indicates that ratios are specified in the GDATA= data set 

SUBJECT= identifies the subjects in the model 

TYPE= specifies the covariance structure 


ALPHA=˛ determines the confidence level (1 ˛) 

CL requests confidence limits for predictors of random effects 

G displays the estimated G matrix 

GC displays the Cholesky root (lower) of estimated G matrix 

GCI displays the inverse Cholesky root (lower) of estimated G matrix 

GCORR displays the correlation matrix corresponding to estimated G matrix 

GI displays the inverse of the estimated G matrix 

SOLUTION displays solutions b of the G-side random effects 

V displays blocks of the estimated V matrix 

VC displays the lower-triangular Cholesky root of blocks of the estimated 

V matrix 

VCI displays the inverse Cholesky root of blocks of the estimated V 

matrix 

VCORR displays the correlation matrix corresponding to blocks of the estimated 

V matrix 

VI displays the inverse of the blocks of the estimated V matrix 

You can specify the following options in the RANDOM statement after a slash (/). 

ALPHA=number 

requests that a t-type confidence interval be constructed for each of the random-effect estimates 

with confidence level 1 number. The value of number must be between 0 and 1; the 

default is 0.05. 

CL 

G 

GC 

GCI 

requests that t-type confidence limits be constructed for each of the random-effect estimates. 

The confidence level is 0.95 by default; this can be changed with the ALPHA= option. 

requests that the estimated G matrix be displayed. PROC MIXED displays blanks for values 

that are 0. If you specify the SUBJECT= option, then the block of the G matrix corresponding 

to the first subject is displayed. For ODS purposes, the name of the table is “G.” 

displays the lower-triangular Cholesky root of the estimated G matrix according to the rules 

listed under the G option. For ODS purposes, the name of the table is “CholG.” 

displays the inverse Cholesky root of the estimated G matrix according to the rules listed 

under the G option. For ODS purposes, the name of the table is “InvCholG.”


GCORR 

displays the correlation matrix corresponding to the estimated G matrix according to the rules 

listed under the G option. For ODS purposes, the name of the table is “GCorr.” 

GDATA=SAS-data-set 

requests that the G matrix be read in from a SAS data set. This G matrix is assumed to 

be known; therefore, only R-side parameters from effects in the REPEATED statement are 

included in the Newton-Raphson iterations. If no REPEATED statement is specified, then 

only a residual variance is estimated. 

GI 

GROUP=effect 

The information in the GDATA= data set can appear in one of two ways. The first is a 

sparse representation for which you include Row, Col, and Value variables to indicate the row, 

column, and value of G, respectively. All unspecified locations are assumed to be 0. The 

second representation is for dense matrices. In it you include Row and Col1–Coln variables to 

indicate, respectively, the row and columns of G, which is a symmetric matrix of order n. For 

both representations, you must specify effects in the RANDOM statement that generate a Z 

matrix that contains n columns. (See Example 56.4.) 

If you have more than one RANDOM statement, only one GDATA= option is required in any 

one of them, and the data set you specify must contain the entire G matrix defined by all of 

the RANDOM statements. 

If the GDATA= data set contains variance ratios instead of the variances themselves, then use 

the RATIOS option. 

Known parameters of G can also be input by using the PARMS statement with the HOLD= 

option. 

displays the inverse of the estimated G matrix according to the rules listed under the G option. 

For ODS purposes, the name of the table is “InvG.” 

GRP=effect 

defines an effect specifying heterogeneity in the covariance structure of G. All observations 

having the same level of the group effect have the same covariance parameters. Each new 

level of the group effect produces a new set of covariance parameters with the same structure 

as the original group. You should exercise caution in defining the group effect, because 

strange covariance patterns can result from its misuse. Also, the group effect can greatly 

increase the number of estimated covariance parameters, which can adversely affect the optimization 

process. 

Continuous variables are permitted as arguments to the GROUP= option. PROC MIXED 

does not sort by the values of the continuous variable; rather, it considers the data to be 

from a new subject or group whenever the value of the continuous variable changes from the 

previous observation. Using a continuous variable decreases execution time for models with 

a large number of subjects or groups and also prevents the production of a large “Class Level 

Information” table. 

LDATA=SAS-data-set 

reads the coefficient matrices associated with the TYPE=LIN(number) option. The data set


must contain the variables Parm, Row, Col1–Coln or Parm, Row, Col, Value. The Parm variable 

denotes which of the number coefficient matrices is currently being constructed, and the Row, 

Col1–Coln, or Row, Col, Value variables specify the matrix values, as they do with the GDATA= 

option. Unspecified values of these matrices are set equal to 0. 

NOFULLZ 

eliminates the columns in Z corresponding to missing levels of random effects involving 

CLASS variables. By default, these columns are included in Z. 

RATIOS 

indicates that ratios with the residual variance are specified in the GDATA= data set instead of 

the covariance parameters themselves. The default GDATA= data set contains the individual 

covariance parameters. 

SOLUTION 

S 

requests that the solution for the random-effects parameters be produced. Using notation 

from the section “Mixed Models Theory” on page 3962, these estimates are the empirical 

best linear unbiased predictors (EBLUPs) b D bGZ 0bV 1 .y Xbˇ/. They can be useful for 

comparing the random effects from different experimental units and can also be treated as 

residuals in performing diagnostics for your mixed model. 

The numbers displayed in the SE Pred column of the “Solution for Random Effects” table 

are not the standard errors of the b displayed in the Estimate column; rather, they are the 

standard errors of predictions bi i, where bi is the ith EBLUP and i is the ith randomeffect 

parameter. 

SUBJECT=effect 

SUB=effect 

identifies the subjects in your mixed model. Complete independence is assumed across subjects; 

thus, for the RANDOM statement, the SUBJECT= option produces a block-diagonal 

structure in G with identical blocks. The Z matrix is modified to accommodate this block 

diagonality. In fact, specifying a subject effect is equivalent to nesting all other effects in the 

RANDOM statement within the subject effect. 

Continuous variables are permitted as arguments to the SUBJECT= option. PROC MIXED 






When you specify the SUBJECT= option and a classification random effect, computations 

are usually much quicker if the levels of the random effect are duplicated within each level of 

the SUBJECT= effect. 

TYPE=covariance-structure 

specifies the covariance structure of G. Valid values for covariance-structure and their descriptions 

are listed in Table 56.13 and Table 56.14. Although a variety of structures are


available, most applications call for either TYPE=VC or TYPE=UN. The TYPE=VC (variance 

components) option is the default structure, and it models a different variance component 

for each random effect. 

The TYPE=UN (unstructured) option is useful for correlated random coefficient models. For 

example, the following statement specifies a random intercept-slope model that has different 

variances for the intercept and slope and a covariance between them: 

random intercept age / type=un subject=person; 

You can also use TYPE=FA0(2) here to request a G estimate that is constrained to be nonnegative 

definite. 

If you are constructing your own columns of Z with continuous variables, you can use the 

TYPE=TOEP(1) structure to group them together to have a common variance component. If 

you want to have different covariance structures in different parts of G, you must use multiple 

RANDOM statements with different TYPE= options. 

V< =value-list > 

requests that blocks of the estimated V matrix be displayed. The first block determined by 

the SUBJECT= effect is the default displayed block. PROC MIXED displays entries that are 

0 as blanks in the table. 

You can optionally use the value-list specification, which indicates the subjects for which 

blocks of V are to be displayed. For example, the following statement displays block matrices 

for the first, third, and seventh persons: 

random int time / type=un subject=person v=1,3,7; 

The table name for ODS purposes is “V.” 

VC< =value-list > 

displays the Cholesky root of the blocks of the estimated V matrix. The value-list specification 

is the same as in the V= option. The table name for ODS purposes is “CholV.” 

VCI< =value-list > 

displays the inverse of the Cholesky root of the blocks of the estimated V matrix. The valuelist 

specification is the same as in the V= option. The table name for ODS purposes is “Inv- 

CholV.” 

VCORR< =value-list > 

displays the correlation matrix corresponding to the blocks of the estimated V matrix. The 

value-list specification is the same as in the V= option. The table name for ODS purposes is 

“VCorr.” 

VI< =value-list > 

displays the inverse of the blocks of the estimated V matrix. The value-list specification is 

the same as in the V= option. The table name for ODS purposes is “InvV.”


REPEATED Statement 

REPEATED < repeated-effect >< / options > ; 

The REPEATED statement is used to specify the R matrix in the mixed model. Its syntax is different 

from that of the REPEATED statement in PROC GLM. If no REPEATED statement is specified, R 

is assumed to be equal to 2 I. 

For many repeated measures models, no repeated effect is required in the REPEATED statement. 

Simply use the SUBJECT= option to define the blocks of R and the TYPE= option to define their 

covariance structure. In this case, the repeated measures data must be similarly ordered for each 

subject, and you must indicate all missing response variables with periods in the input data set unless 

they all fall at the end of a subject’s repeated response profile. These requirements are necessary in 

order to inform PROC MIXED of the proper location of the observed repeated responses. 

Specifying a repeated effect is useful when you do not want to indicate missing values with periods 

in the input data set. The repeated effect must contain only classification variables. Make sure that 

the levels of the repeated effect are different for each observation within a subject; otherwise, PROC 

MIXED constructs identical rows in R corresponding to the observations with the same level. This 

results in a singular R and an infinite likelihood. 

Whether you specify a REPEATED effect or not, the rows of R for each subject are constructed in 

the order in which they appear in the input data set. 

Table 56.12 summarizes important options in the REPEATED statement. All options are subsequently 

discussed in alphabetical order. 

Table 56.12 Summary of Important REPEATED Statement Options 


Construction of Covariance Structure 

GROUP= defines an effect specifying heterogeneity in the R-side covariance 

structure 

LDATA= specifies data set with coefficient matrices for TYPE= LIN 

LOCAL requests that a diagonal matrix be added to R 

LOCALW specifies that only the local effects are weighted 

NOLOCALW specifies that only the nonlocal effects are weighted 

SUBJECT= identifies the subjects in the R-side model 

TYPE= specifies the R-side covariance structure 


HLM produces a table of Hotelling-Lawley-McKeon statistics (McKeon 

1974) 

HLPS produces a table of Hotelling-Lawley-Pillai-Samson statistics (Pillai 

and Samson 1959) 

R displays blocks of the estimated R matrix 

RC display the Cholesky root (lower) of blocks of the estimated R 

matrix



REPEATED Statement ✦ 3949 

RCI displays the inverse Cholesky root (lower) of blocks of the estimated 

R matrix 

RCORR displays the correlation matrix corresponding to blocks of the estimated 

R matrix 

RI displays the inverse of blocks of the estimated R matrix 

You can specify the following options in the REPEATED statement after a slash (/). 

GROUP=effect 

GRP=effect 

defines an effect specifying heterogeneity in the covariance structure of R. All observations 

having the same level of the GROUP effect have the same covariance parameters. Each 

new level of the GROUP effect produces a new set of covariance parameters with the same 

structure as the original group. You should exercise caution in properly defining the GROUP 

effect, because strange covariance patterns can result with its misuse. Also, the GROUP effect 

can greatly increase the number of estimated covariance parameters, which can adversely 

affect the optimization process. 

HLM 

HLPS 

Continuous variables are permitted as arguments to the GROUP= option. PROC MIXED 






produces a table of Hotelling-Lawley-McKeon statistics (McKeon 1974) for all fixed effects 

whose levels change across data having the same level of the SUBJECT= effect (the withinsubject 

fixed effects). This option applies only when you specify a REPEATED statement 

with the TYPE=UN option and no RANDOM statements. For balanced data, this model is 

equivalent to the multivariate model for repeated measures in PROC GLM. 

The Hotelling-Lawley-McKeon statistic has a slightly better F approximation than the 

Hotelling-Lawley-Pillai-Samson statistic (see the description of the HLPS option, which follows). 

Both of the Hotelling-Lawley statistics can perform much better in small samples than 

the default F statistic (Wright 1994). 

Separate tables are produced for Type 1, 2, and 3 tests, according to the ones you select. For 

ODS purposes, the table names are “HLM1,” “HLM2,” and “HLM3,” respectively. 

produces a table of Hotelling-Lawley-Pillai-Samson statistics (Pillai and Samson 1959) for all 

fixed effects whose levels change across data having the same level of the SUBJECT= effect 

(the within-subject fixed effects). This option applies only when you specify a REPEATED 

statement with the TYPE=UN option and no RANDOM statements. For balanced data, this


model is equivalent to the multivariate model for repeated measures in PROC GLM, and this 

statistic is the same as the Hotelling-Lawley Trace statistic produced by PROC GLM. 

Separate tables are produced for Type 1, 2, and 3 tests, according to the ones you select. For 

ODS purposes, the table names are “HLPS1,” “HLPS2,” and “HLPS3,” respectively. 

LDATA=SAS-data-set 

reads the coefficient matrices associated with the TYPE=LIN(number) option. The data set 

must contain the variables Parm, Row, Col1–Coln or Parm, Row, Col, Value. The Parm variable 

denotes which of the number coefficient matrices is currently being constructed, and the 

Row, Col1–Coln, or Row, Col, Value variables specify the matrix values, as they do with the 

RANDOM statement option GDATA=. Unspecified values of these matrices are set equal to 

0. 

LOCAL 

LOCAL=POM(POM-data-set) 

requests that a diagonal matrix be added to R. With just the LOCAL option, this diagonal 

matrix equals 2 I, and 2 becomes an additional variance parameter that PROC MIXED 

profiles out of the likelihood provided that you do not specify the NOPROFILE option in the 

PROC MIXED statement. The LOCAL option is useful if you want to add an observational 

error to a time series structure (Jones and Boadi-Boateng 1991) or a nugget effect to a spatial 

structure (Cressie 1993). 

The LOCAL=EXP() option produces exponential local effects, also known as dispersion 

effects, in a log-linear variance model. These local effects have the form 

2 diagŒexp.Uı/ 

where U is the full-rank design matrix corresponding to the effects that you specify and ı are 

the parameters that PROC MIXED estimates. An intercept is not included in U because it is 

accounted for by 2 . PROC MIXED constructs the full-rank U in terms of 1s and 1s for 

classification effects. Be sure to scale continuous effects in U sensibly. 

The LOCAL=POM(POM-data-set) option specifies the power-of-the-mean structure. This 

structure possesses a variance of the form 2 jx 0 i ˇ j for the ith observation, where xi is the 

ith row of X (the design matrix of the fixed effects) and ˇ is an estimate of the fixed-effects 

parameters that you specify in POM-data-set. 

The SAS data set specified by POM-data-set contains the numeric variable Estimate (in previous 

releases, the variable name was required to be EST), and it has at least as many observations 

as there are fixed-effects parameters. The first p observations of the Estimate variable 

in POM-data-set are taken to be the elements of ˇ , where p is the number of columns of 

X. You must order these observations according to the non-full-rank parameterization of the 

MIXED procedure. One easy way to set up POM-data-set for a ˇ corresponding to ordinary 

least squares is illustrated by the following statements: 

ods output SolutionF=sf; 

proc mixed; 

class a; 

model y = a x / s; 

run;

proc mixed; 

class a; 

model y = a x; 

repeated / local=pom(sf); 

run; 


Note that the generalized least squares estimate of the fixed-effects parameters from the second 

PROC MIXED step usually is not the same as your specified ˇ . However, you can 

iterate the POM fitting until the two estimates agree. Continuing from the previous example, 

the statements for performing one step of this iteration are as follows: 

ods output SolutionF=sf1; 

proc mixed; 

class a; 

model y = a x / s; 

repeated / local=pom(sf); 

run; 

proc compare brief data=sf compare=sf1; 

var estimate; 

run; 

data sf; 

set sf1; 

run; 

Unfortunately, this iterative process does not always converge. For further details, refer to the 

description of pseudo-likelihood in Chapter 3 of Carroll and Ruppert (1988). 

LOCALW 

specifies that only the local effects and no others be weighted. By default, all effects are 

weighted. The LOCALW option is used in connection with the WEIGHT statement and the 

LOCAL option in the REPEATED statement. 

NONLOCALW 

specifies that only the nonlocal effects and no others be weighted. By default, all effects are 

weighted. The NONLOCALW option is used in connection with the WEIGHT statement and 

the LOCAL option in the REPEATED statement. 

R< =value-list > 

requests that blocks of the estimated R matrix be displayed. The first block determined by 

the SUBJECT= effect is the default displayed block. PROC MIXED displays blanks for 

value-lists that are 0. 

The value-list indicates the subjects for which blocks of R are to be displayed. For example, 

the following statement displays block matrices for the first, third, and fifth persons: 

repeated / type=cs subject=person r=1,3,5; 

See the PARMS statement for the possible forms of value-list. The table name for ODS 

purposes is “R.”


RC< =value-list > 

produces the Cholesky root of blocks of the estimated R matrix. The value-list specification 

is the same as with the R option. The table name for ODS purposes is “CholR.” 

RCI< =value-list > 

produces the inverse Cholesky root of blocks of the estimated R matrix. The value-list specification 

is the same as with the R option. The table name for ODS purposes is “InvCholR.” 

RCORR< =value-list > 

produces the correlation matrix corresponding to blocks of the estimated R matrix. The 

value-list specification is the same as with the R option. The table name for ODS purposes is 

“RCorr.” 

RI< =value-list > 

produces the inverse of blocks of the estimated R matrix. The value-list specification is the 

same as with the R option. The table name for ODS purposes is “InvR.” 

SSCP 

requests that an unstructured R matrix be estimated from the sum-of-squares-andcrossproducts 

matrix of the residuals. It applies only when you specify TYPE=UN and 

have no RANDOM statements. Also, you must have a sufficient number of subjects for the 

estimate to be positive definite. 

This option is useful when the size of the blocks of R is large (for example, greater than 10) 

and you want to use or inspect an unstructured estimate that is much quicker to compute than 

the default REML estimate. The two estimates will agree for certain balanced data sets when 

you have a classification fixed effect defined across all time points within a subject. 

SUBJECT=effect 

SUB=effect 

identifies the subjects in your mixed model. Complete independence is assumed across subjects; 

therefore, the SUBJECT= option produces a block-diagonal structure in R with identical 

blocks. When the SUBJECT= effect consists entirely of classification variables, the 

blocks of R correspond to observations sharing the same level of that effect. These blocks are 

sorted according to this effect as well. 

Continuous variables are permitted as arguments to the SUBJECT= option. PROC MIXED 






If you want to model nonzero covariance among all of the observations in your SAS data set, 

specify SUBJECT=INTERCEPT to treat the data as if they are all from one subject. However, 

be aware that in this case PROC MIXED manipulates an R matrix with dimensions equal to 

the number of observations. If no SUBJECT= effect is specified, then every observation is 

assumed to be from a different subject and R is assumed to be diagonal. For this reason, you 

usually want to use the SUBJECT= option in the REPEATED statement.


TYPE=covariance-structure 

specifies the covariance structure of the R matrix. The SUBJECT= option defines the 

blocks of R, and the TYPE= option specifies the structure of these blocks. Valid values 

for covariance-structure and their descriptions are provided in Table 56.13 and Table 56.14. 

The default structure is VC. 

Table 56.13 Covariance Structures 

Structure Description Parms .i; j /th element 

ANTE(1) Ante-dependence 2t 1 i 

Qj 1 

j kDi k 

AR(1) Autoregressive(1) 2 2 ji j j 

ARH(1) Heterogeneous AR(1) t C 1 i j 

ARMA(1,1) ARMA(1,1) 3 2 Œ ji j j 1 1.i ¤ j / C 1.i D j / 

ji j j 

CS Compound Symmetry 2 1 C 2 1.i D j / 

CSH Heterogeneous CS t C 1 i j Œ 1.i ¤ j / C 1.i D j / 

FA(q) Factor Analytic 

FA0(q) No Diagonal FA 

FA1(q) Equal Diagonal FA 

q 

2 .2t q C 1/ C t †min.i;j;q/ 

kD1 ik jk C 2 i 1.i D j / 

q 

2 .2t q C 1/ †min.i;j;q/ 

kD1 ik jk 

q 

2 .2t q C 1/ C 1 †min.i;j;q/ 

kD1 ik jk C 21.i D j / 

HF Huynh-Feldt t C 1 . 2 i C 2 j 

/=2 C 1.i ¤ j / 

LIN(q) General Linear q † q 

kD1 kAij 

TOEP Toeplitz t ji j jC1 

TOEP(q) Banded Toeplitz q ji j jC11.ji j j < q/ 

TOEPH Heterogeneous TOEP 2t 1 i j ji j j 

TOEPH(q) Banded Hetero TOEP t C q 1 i j ji j j1.ji j j < q/ 

UN Unstructured t.t C 1/=2 ij 

UN(q) Banded 

q 

2 .2t q C 1/ ij 1.ji j j < q/ 

UNR Unstructured Corrs t.t C 1/=2 i j max.i;j / min.i;j / 

UNR(q) Banded Correlations 

q 

2 .2t q C 1/ i j max.i;j / min.i;j / 

ji2 j2j 

UN@AR(1) Direct Product AR(1) t1.t1 C 1/=2 C 1 i1j1 

UN@CS Direct Product CS t1.t1 C 1/=2 C 1 

UN@UN Direct Product UN t1.t1 C 1/=2 C 1;i1j1 2;i2j2 

t2.t2 C 1/=2 1 

8 

< 

: 

i1j1 

i2 D j2 

2 i1j1 i2 6D j2 

0 2 1 

2 

VC Variance Components q 1.i D j / 

k 

and i corresponds to kth effect 

In Table 56.13, “Parms” is the number of covariance parameters in the structure, t is the 

overall dimension of the covariance matrix, and 1.A/ equals 1 when A is true and 0 otherwise. 

For example, 1.i D j / equals 1 when i D j and 0 otherwise, and 1.ji j j < q/ equals 

1 when ji j j < q and 0 otherwise. For the TOEPH structures, 0 D 1, and for the UNR


structures, ii D 1 for all i. For the direct product structures, the subscripts “1” and “2” refer 

to the first and second structure in the direct product, respectively, and i1 D int..i C t2 

1/=t2/, j1 D int..j C t2 1/=t2/, i2 D mod.i 1; t2/ C 1, and j2 D mod.j 1; t2/ C 1. 

Table 56.14 Spatial Covariance Structures 

Structure Description Parms .i; j /th element 

SP(EXP)(c-list ) Exponential 2 2 expf dij = g 

SP(EXPA)(c-list ) Anisotropic Exponential 2c C 1 2 Q c 

kD1 expf kd.i; j; k/ pkg 

SP(EXPGA)(c1 c2) 2D Exponential, 4 2 expf dij . ; /= g 

Geometrically Anisotropic 

SP(GAU)(c-list ) Gaussian 2 2 expf d 2 ij = 2 g 

SP(GAUGA)(c1 c2) 2D Gaussian, 4 2 expf dij . ; / 2 = 2 g 

Geometrically Anisotropic 

SP(LIN)(c-list ) Linear 2 2 .1 dij / 1. dij 1/ 

SP(LINL)(c-list ) Linear Log 2 2 .1 log.dij // 

1. log.dij / 1/ 

SP(MATERN)(c-list) Matérn 3 2 1 

. / 

SP(MATHSW)(c-list) Matérn 3 2 1 

. / 

(Handcock-Stein-Wallis) 

SP(POW)(c-list) Power 2 2 dij 

SP(POWA)(c-list) Anisotropic Power c C 1 2 d.i;j;1/ 

1 

dij 

2 

p 

dij 

d.i;j;2/ 

2 

2K .dij = / 

2K 2dij 

p 

: : : d.i;j;c/ 

c 

SP(SPH)(c-list ) Spherical 2 2 3dij 

Œ1 . 2 / C . 

2 3 / 1.dij 

SP(SPHGA)(c1 c2) 2D Spherical, 4 2 Œ1 . 3dij . ; / 

2 

Geometrically Anisotropic 1.dij . ; / / 

d 3 

ij 

/ C . dij . ; / 3 

2 3 / 

In Table 56.14, c-list contains the names of the numeric variables used as coordinates of the 

location of the observation in space, and dij is the Euclidean distance between the ith and 

jth vectors of these coordinates, which correspond to the ith and jth observations in the input 

data set. For SP(POWA) and SP(EXPA), c is the number of coordinates, and d.i; j; k/ is the 

absolute distance between the kth coordinate, k D 1; : : : ; c, of the ith and jth observations in 

the input data set. For the geometrically anisotropic structures SP(EXPGA), SP(GAUGA), 

and SP(SPHGA), exactly two spatial coordinate variables must be specified as c1 and c2. 

Geometric anisotropy is corrected by applying a rotation and scaling to the coordinate 

system, and dij . ; / represents the Euclidean distance between two points in the transformed 

space. SP(MATERN) and SP(MATHSW) represent covariance structures in a class defined 

by Matérn (see Matérn 1986, Handcock and Stein 1993, Handcock and Wallis 1994). The 

function K is the modified Bessel function of the second kind of (real) order > 0; the 

parameter governs the smoothness of the process (see below for more details). 

Table 56.15 lists some examples of the structures in Table 56.13 and Table 56.14. 

/

Table 56.15 Covariance Structure Examples 

Description Structure Example 

2 

Variance 

Components 

Compound 

Symmetry 

VC (default) 

CS 

Unstructured UN 

Banded Main 

Diagonal 

First-Order 

Autoregressive 

UN(1) 

AR(1) 

Toeplitz TOEP 

Toeplitz with 

Two Bands 

Spatial 

Power 

Heterogeneous 

AR(1) 

First-Order 

Autoregressive 

Moving-Average 

TOEP(2) 

SP(POW)(c) 

ARH(1) 

ARMA(1,1) 

6 

4 

2 

6 

4 

2 

6 

4 

2 

6 

4 

2 

2 

B 0 0 0 

0 2 B 0 0 

0 0 2 AB 0 

0 0 0 2 AB 

3 

7 

5 

2 C 1 1 1 1 

2 

1 C 1 1 1 

2 

1 1 C 1 1 

2 

1 1 1 C 1 

3 

2 

1 21 31 41 

2 

21 2 32 42 

2 

31 32 3 43 

2 

41 42 43 4 

3 

2 

1 0 0 0 

2 0 2 0 0 7 

2 0 0 3 0 5 

2 0 0 0 4 

2 

3 

1 2 3 

2 

6 

4 

6 

4 

1 

2 

2 1 

3 2 1 

2 

1 2 3 

2 

1 1 2 

2 

2 1 1 

2 

3 2 1 

3 

7 

5 

2 

3 

2 

1 0 0 

6 2 

6 1 1 0 7 

4 0 2 5 

1 1 

0 0 2 

1 

2 

2 

2 

6 

4 

6 

4 

7 

5 

7 

5 

1 d12 d13 d14 

d21 1 d23 d24 

d31 d32 1 d34 

d41 d42 d43 1 


3 

7 

5 

2 

1 1 2 1 3 2 

1 4 3 

2 

2 1 2 2 3 2 4 2 

3 1 2 

2 

3 2 3 3 4 

4 1 3 

2 

4 2 4 3 4 

2 

3 2 

1 

6 

2 6 

4 

1 

1 

7 

5 

2 1 

3 

7 

5 

3 

7 

5



Description Structure Example 

2 

Heterogeneous 

CS 

First-Order 

Factor 

Analytic 

CSH 

FA(1) 

Huynh-Feldt HF 

First-Order 

Ante-dependence 

Heterogeneous 

Toeplitz 

Unstructured 

Correlations 

Direct Product 

AR(1) 

ANTE(1) 

TOEPH 

UNR 

UN@AR(1) 

6 

4 

2 

6 

4 

2 

6 

4 

2 

4 

2 

6 

4 

2 

6 

4 

2 

6 

4 

2 

1 

2 1 

3 1 

4 1 

1 2 

2 

2 

3 2 

4 2 

1 3 

2 3 

2 

3 

4 3 

1 4 

2 4 

3 4 

2 

4 

7 

5 

2 

1 C d1 

2 1 

1 2 

2 

2 

1 3 1 4 

C d2 

3 1 3 2 

2 3 

2 

3 

2 4 

C d3 

4 1 4 2 4 3 

3 4 

2 

4 

2 

1 

2 

2 C 2 1 

2 

2 

3 C 2 1 

2 

1 C 2 2 

2 

2 

2 

2 

3 C 2 2 

2 

3 

2 

1 C 2 3 

2 

2 

2 C 2 3 

2 

2 

3 

2 

2 

1 

2 1 1 

3 1 2 1 

1 

3 

2 

2 

2 

2 

1 

2 

1 3 1 2 

2 3 2 

2 

3 

3 

5 

C d4 

3 

7 

5 

3 

3 

7 

5 

2 

3 

4 

2 

1 

1 

1 

1 

1 

2 

3 

1 

3 

4 

2 

2 

2 

2 

2 

1 

1 

2 

1 

2 

4 

3 

3 

2 

3 

3 

2 

1 

1 

1 

2 

3 

4 

4 

4 

2 

4 

3 

7 27 

5 1 

2 

1 

2 1 21 

3 1 31 

4 1 41 

2 

1 21 

2 

21 2 

1 2 21 

2 

2 

3 2 32 

4 2 42 

2 

1 

˝ 4 

1 

2 

4 

3 31 

3 32 

2 

3 

3 43 

3 2 

5 D 

1 

2 

3 

4 41 

4 42 

4 43 

2 

4 

1 

2 1 

3 

7 

5 

2 2 2 

1 1 1 2 

21 21 21 2 

2 2 2 

1 1 1 21 21 21 

2 

1 2 2 2 

1 1 21 2 

21 21 

21 21 21 2 2 2 2 

2 2 2 2 

2 2 2 

21 21 21 2 2 2 

21 2 

2 

21 21 2 2 2 2 

2 2 

The following provides some further information about these covariance structures: 

TYPE=ANTE(1) specifies the first-order antedependence structure (see Kenward 1987, Patel 

1991, and Macchiavelli and Arnold 1994). In Table 56.13, 2 i is the ith 

variance parameter, and k is the kth autocorrelation parameter satisfying 

j kj < 1. 

3 

7 

5


TYPE=AR(1) specifies a first-order autoregressive structure. PROC MIXED imposes the 

constraint j j < 1 for stationarity. 

TYPE=ARH(1) specifies a heterogeneous first-order autoregressive structure. As with 

TYPE=AR(1), PROC MIXED imposes the constraint j j < 1 for stationarity. 

TYPE=ARMA(1,1) specifies the first-order autoregressive moving-average structure. In 

Table 56.13, is the autoregressive parameter, models a moving-average 

component, and 2 is the residual variance. In the notation of Fuller (1976, 

p. 68), D 1 and 

D .1 C b1 1/. 1 C b1/ 

1 C b 2 1 C 2b1 1 

The example in Table 56.15 and jb1j < 1 imply that 

b1 D ˇ p ˇ 2 4˛ 2 

2˛ 

where ˛ D and ˇ D 1 C 2 2 . PROC MIXED imposes the 

constraints j j < 1 and j j < 1 for stationarity, although for some values 

of and in this region the resulting covariance matrix is not positive 

definite. When the estimated value of becomes negative, the computed 

covariance is multiplied by cos. dij / to account for the negativity. 

TYPE=CS specifies the compound-symmetry structure, which has constant variance 

and constant covariance. 

TYPE=CSH specifies the heterogeneous compound-symmetry structure. This structure 

has a different variance parameter for each diagonal element, and it 

uses the square roots of these parameters in the off-diagonal entries. In 

Table 56.13, 2 i is the ith variance parameter, and is the correlation parameter 

satisfying j j < 1. 

TYPE=FA(q) specifies the factor-analytic structure with q factors (Jennrich and 

Schluchter 1986). This structure is of the form ƒƒ 0 C D, where ƒ 

is a t q rectangular matrix and D is a t t diagonal matrix with t 

different parameters. When q > 1, the elements of ƒ in its upper-right 

corner (that is, the elements in the ith row and j th column for j > i) are 

set to zero to fix the rotation of the structure. 

TYPE=FA0(q) is similar to the FA(q) structure except that no diagonal matrix D is included. 

When q < t—that is, when the number of factors is less than 

the dimension of the matrix—this structure is nonnegative definite but not 

of full rank. In this situation, you can use it for approximating an unstructured 

G matrix in the RANDOM statement or for combining with the 

LOCAL option in the REPEATED statement. When q D t, you can use 

this structure to constrain G to be nonnegative definite in the RANDOM 

statement. 

TYPE=FA1(q) is similar to the FA(q) structure except that all of the elements in D are 

constrained to be equal. This offers a useful and more parsimonious alternative 

to the full factor-analytic structure.


TYPE=HF specifies the Huynh-Feldt covariance structure (Huynh and Feldt 1970). 

This structure is similar to the CSH structure in that it has the same number 

of parameters and heterogeneity along the main diagonal. However, it 

constructs the off-diagonal elements by taking arithmetic rather than geometric 

means. 

You can perform a likelihood ratio test of the Huynh-Feldt conditions 

by running PROC MIXED twice, once with TYPE=HF and once with 

TYPE=UN, and then subtracting their respective values of 2 times the 

maximized likelihood. 

If PROC MIXED does not converge under your Huynh-Feldt model, you 

can specify your own starting values with the PARMS statement. The 

default MIVQUE(0) starting values can sometimes be poor for this structure. 

A good choice for starting values is often the parameter estimates 

corresponding to an initial fit that uses TYPE=CS. 

TYPE=LIN(q) specifies the general linear covariance structure with q parameters. This 

structure consists of a linear combination of known matrices that are input 

with the LDATA= option. This structure is very general, and you need to 

make sure that the variance matrix is positive definite. By default, PROC 

MIXED sets the initial values of the parameters to 1. You can use the 

PARMS statement to specify other initial values. 

TYPE=SIMPLE is an alias for TYPE=VC. 

TYPE=SP(EXPA)(c-list) specifies the spatial anisotropic exponential structure, where c-list 

is a list of variables indicating the coordinates. This structure has .i; j /th 

element equal to 

2 

cY 

expf kd.i; j; k/ pkg kD1 

where c is the number of coordinates and d.i; j; k/ is the absolute distance 

between the kth coordinate (k D 1; : : : ; c) of the ith and j th observations 

in the input data set. There are 2c C 1 parameters to be estimated: k, p k 

(k D 1; : : : ; c), and 2 . 

You might want to constrain some of the EXPA parameters to known values. 

For example, suppose you have three coordinate variables C1, C2, 

and C3 and you want to constrain the powers p k to equal 2, as in Sacks et 

al. (1989). Suppose further that you want to model covariance across the 

entire input data set and you suspect the k and 2 estimates are close to 

3, 4, 5, and 1, respectively. Then specify the following statements: 

repeated / type=sp(expa)(c1 c2 c3) 

subject=intercept; 

parms (3) (4) (5) (2) (2) (2) (1) / 

hold=4,5,6;

TYPE=SP(EXPGA)(c1 c2) 

TYPE=SP(GAUGA)(c1 c2) 


TYPE=SP(SPHGA)(c1 c2) specify modifications of the isotropic SP(EXP), SP(SPH), and 

SP(GAU) covariance structures that allow for geometric anisotropy in two 

dimensions. The coordinates are specified by the variables c1 and c2. 

If the spatial process is geometrically anisotropic in c D Œci1; ci2, then it 

is isotropic in the coordinate system 

Ac D 

TYPE=SP(MATERN)(c-list ) 

1 0 

0 

cos sin 

sin cos 

c D c 

for a properly chosen angle and scaling factor . Elliptical isocorrelation 

contours are thereby transformed to spherical contours, adding two parameters 

to the respective isotropic covariance structures. Euclidean distances 

(see Table 56.14) are expressed in terms of c . 

The angle of the clockwise rotation is reported in radians, 0 2 . 

The scaling parameter represents the ratio of the range parameters in the 

direction of the major and minor axis of the correlation contours. In other 

words, following a rotation of the coordinate system by angle , isotropy 

is achieved by compressing or magnifying distances in one coordinate by 

the factor . 

Fixing D 1:0 reduces the models to isotropic ones for any angle of 

rotation. If the scaling parameter is held constant at 1.0, you should also 

hold constant the angle of rotation, as in the following statements: 

repeated / type=sp(expga)(gxc gyc) 

subject=intercept; 

parms (6) (1.0) (0.0) (1) / hold=2,3; 

If is fixed at any other value than 1.0, the angle of rotation can be estimated. 

Specifying a starting grid of angles and scaling factors can considerably 

improve the convergence properties of the optimization algorithm 

for these models. Only a single random effect with geometrically 

anisotropic structure is permitted. 

TYPE=SP(MATHSW)(c-list ) specifies covariance structures in the Matérn class of covariance 

functions (Matérn 1986). Two observations for the same subject 

(block of R) that are Euclidean distance dij apart have covariance 

2 1 

. / 

dij 

2 

2K .dij = / > 0; > 0 

where K is the modified Bessel function of the second kind of (real) order 

> 0. The smoothness (continuity) of a stochastic process with covariance 

function in this class increases with . The Matérn class thus enables 

data-driven estimation of the smoothness properties. The covariance 

is identical to the exponential model for D 0:5 (TYPE=SP(EXP)(clist)), 

while for D 1 the model advocated by Whittle (1954) results.


TYPE=SP(POW)(c-list) 

As ! 1 the model approaches the gaussian covariance structure 

(TYPE=SP(GAU)(c-list)). 

The MATHSW structure represents the Matérn class in the parameterization 

of Handcock and Stein (1993) and Handcock and Wallis (1994), 

2 1 

. / 

p 

dij 

2K 2dij 

p 

Since computation of the function K and its derivatives is numerically 

very intensive, fitting models with Matérn covariance structures can be 

more time-consuming than with other spatial covariance structures. Good 

starting values are essential. 

TYPE=SP(POWA)(c-list) specifies the spatial power structures. When the estimated 

value of becomes negative, the computed covariance is multiplied by 

cos. dij / to account for the negativity. 

TYPE=TOEP specifies a banded Toeplitz structure. This can be viewed as a movingaverage 

structure with order equal to q 1. The TYPE=TOEP option is 

a full Toeplitz matrix, which can be viewed as an autoregressive structure 

with order equal to the dimension of the matrix. The specification 

TYPE=TOEP(1) is the same as 2 I , where I is an identity matrix, and 

it can be useful for specifying the same variance component for several 

effects. 

TYPE=TOEPH specifies a heterogeneous banded Toeplitz structure. In 

Table 56.13, 2 i is the ith variance parameter and j is the j th correlation 

parameter satisfying j j j < 1. If you specify the order parameter q, 

then PROC MIXED estimates only the first q bands of the matrix, setting 

all higher bands equal to 0. The option TOEPH(1) is equivalent to both 

the UN(1) and UNR(1) options. 

TYPE=UN specifies a completely general (unstructured) covariance matrix parameterized 

directly in terms of variances and covariances. The variances are 

constrained to be nonnegative, and the covariances are unconstrained. This 

structure is not constrained to be nonnegative definite in order to avoid 

nonlinear constraints; however, you can use the FA0 structure if you want 

this constraint to be imposed by a Cholesky factorization. If you specify 

the order parameter q, then PROC MIXED estimates only the first q bands 

of the matrix, setting all higher bands equal to 0. 

TYPE=UNR specifies a completely general (unstructured) covariance matrix parameterized 

in terms of variances and correlations. This structure fits the same 

model as the TYPE=UN(q) option but with a different parameterization. 

The ith variance parameter is 2 i . The parameter jk is the correlation between 

the j th and kth measurements; it satisfies j jkj < 1. If you specify 

the order parameter r, then PROC MIXED estimates only the first q bands 

of the matrix, setting all higher bands equal to zero.

TYPE=UN@AR(1) 

TYPE=UN@CS 


TYPE=UN@UN specify direct (Kronecker) product structures designed for multivariate repeated 

measures (see Galecki 1994). These structures are constructed by 

taking the Kronecker product of an unstructured matrix (modeling covariance 

across the multivariate observations) with an additional covariance 

matrix (modeling covariance across time or another factor). The upper-left 

value in the second matrix is constrained to equal 1 to identify the model. 

See the SAS/IML User’s Guide for more details about direct products. 

To use these structures in the REPEATED statement, you must specify 

two distinct REPEATED effects, both of which must be included in the 

CLASS statement. The first effect indicates the multivariate observations, 

and the second identifies the levels of time or some additional factor. Note 

that the input data set must still be constructed in “univariate” format; that 

is, all dependent observations are still listed observation-wise in one single 

variable. Although this construction provides for general modeling possibilities, 

it forces you to construct variables indicating both dimensions of 

the Kronecker product. 

For example, suppose your observed data consist of heights and weights of 

several children measured over several successive years. Your input data 

set should then contain variables similar to the following: 

Y, all of the heights and weights, with a separate observation for each 

Var, indicating whether the measurement is a height or a weight 

Year, indicating the year of measurement 

Child, indicating the child on which the measurement was taken 

Your PROC MIXED statements for a Kronecker AR(1) structure across 

years would then be as follows: 

proc mixed; 

class Var Year Child; 

model Y = Var Year Var*Year; 

repeated Var Year / type=un@ar(1) 

subject=Child; 

run; 

You should nearly always want to model different means for the multivariate 

observations; hence the inclusion of Var in the MODEL statement. The 

preceding mean model consists of cell means for all combinations of VAR 

and YEAR. 

TYPE=VC specifies standard variance components and is the default structure for 

both the RANDOM and REPEATED statements. In the RANDOM 

statement, a distinct variance component is assigned to each effect. In 

the REPEATED statement, this structure is usually used only with the 

GROUP= option to specify a heterogeneous variance model. 

Jennrich and Schluchter (1986) provide general information about the use of covariance structures, 

and Wolfinger (1996) presents details about many of the heterogeneous structures.


Modeling with spatial covariance structures is discussed in many sources, for example, Marx 

and Thompson (1987), Zimmerman and Harville (1991), Cressie (1993), Brownie, Bowman, 

and Burton (1993), Stroup, Baenziger, and Mulitze (1994), Brownie and Gumpertz (1997), 

Gotway and Stroup (1997), Chilès and Delfiner (1999), Schabenberger and Gotway (2005), 

and Littell et al. (2006). 

WEIGHT Statement 

WEIGHT variable ; 

If you do not specify a REPEATED statement, the WEIGHT statement operates exactly like the 

one in PROC GLM. In this case PROC MIXED replaces X 0 X and Z 0 Z with X 0 WX and Z 0 WZ, 

where W is the diagonal weight matrix. If you specify a REPEATED statement, then the WEIGHT 

statement replaces R with LRL, where L is a diagonal matrix with elements W 1=2 . Observations 

with nonpositive or missing weights are not included in the PROC MIXED analysis. 

Details: MIXED Procedure 

Mixed Models Theory 

This section provides an overview of a likelihood-based approach to general linear mixed models. 

This approach simplifies and unifies many common statistical analyses, including those involving 

repeated measures, random effects, and random coefficients. The basic assumption is that the data 

are linearly related to unobserved multivariate normal random variables. For extensions to nonlinear 

and nonnormal situations see the documentation of the GLIMMIX and NLMIXED procedures. 

Additional theory and examples are provided in Littell et al. (2006), Verbeke and Molenberghs 

(1997, 2000), and Brown and Prescott (1999). 

Matrix Notation 

Suppose that you observe n data points y1; : : : ; yn and that you want to explain them by using 

n values for each of p explanatory variables x11; : : : ; x1p, x21; : : : ; x2p, : : : ; xn1; : : : ; xnp. The 

xij values can be either regression-type continuous variables or dummy variables indicating class 

membership. The standard linear model for this setup is 

yi D 

pX 

j D1 

xij ˇj C i 

i D 1; : : : ; n

Mixed Models Theory ✦ 3963 

where ˇ1; : : : ; ˇp are unknown fixed-effects parameters to be estimated and 1; : : : ; n are unknown 

independent and identically distributed normal (Gaussian) random variables with mean 0 and variance 

2 . 

The preceding equations can be written simultaneously by using vectors and a matrix, as follows: 

2 

y1 

6 y2 

6 

4 : 

3 

7 

5 D 

2 

x11 

6 x21 

6 

4 : 

x12 

x22 

: 

: : : 

: : : 

x1p 

x2p 

: 

3 2 

7 6 

7 6 

7 6 

5 4 

ˇ1 

ˇ2 

: 

3 

7 

5 C 

2 

6 

4 

1 

2 

: 

3 

7 

5 

yn 

xn1 xn2 : : : xnp 

For convenience, simplicity, and extendability, this entire system is written as 

y D Xˇ C 

ˇp 

where y denotes the vector of observed yi’s, X is the known matrix of xij ’s, ˇ is the unknown fixedeffects 

parameter vector, and is the unobserved vector of independent and identically distributed 

Gaussian random errors. 

In addition to denoting data, random variables, and explanatory variables in the preceding fashion, 

the subsequent development makes use of basic matrix operators such as transpose ( 0 ), inverse ( 1 ), 

generalized inverse ( ), determinant (j j), and matrix multiplication. See Searle (1982) for details 

about these and other matrix techniques. 

Formulation of the Mixed Model 

The previous general linear model is certainly a useful one (Searle 1971), and it is the one fitted by 

the GLM procedure. However, many times the distributional assumption about is too restrictive. 

The mixed model extends the general linear model by allowing a more flexible specification of the 

covariance matrix of . In other words, it allows for both correlation and heterogeneous variances, 

although you still assume normality. 

The mixed model is written as 

y D Xˇ C Z C 

where everything is the same as in the general linear model except for the addition of the known 

design matrix, Z, and the vector of unknown random-effects parameters, . The matrix Z can 

contain either continuous or dummy variables, just like X. The name mixed model comes from the 

fact that the model contains both fixed-effects parameters, ˇ, and random-effects parameters, . 

See Henderson (1990) and Searle, Casella, and McCulloch (1992) for historical developments of 

the mixed model. 

A key assumption in the foregoing analysis is that and are normally distributed with 

E D 0 

0 

Var D 

G 0 

0 R 

n


The variance of y is, therefore, V D ZGZ 0 C R. You can model V by setting up the random-effects 

design matrix Z and by specifying covariance structures for G and R. 

Note that this is a general specification of the mixed model, in contrast to many texts and articles 

that discuss only simple random effects. Simple random effects are a special case of the general 

specification with Z containing dummy variables, G containing variance components in a diagonal 

structure, and R D 2 In, where In denotes the n n identity matrix. The general linear model is a 

further special case with Z D 0 and R D 2 In. 

The following two examples illustrate the most common formulations of the general linear mixed 

model. 

Example: Growth Curve with Compound Symmetry 

Suppose that you have three growth curve measurements for s individuals and that you want to fit 

an overall linear trend in time. Your X matrix is as follows: 

2 3 

1 1 

6 1 2 7 

6 1 3 7 

6 7 

X D 6 : : 7 

6 1 1 7 

4 1 2 5 

1 3 

The first column (coded entirely with 1s) fits an intercept, and the second column (coded with times 

of 1; 2; 3) fits a slope. Here, n D 3s and p D 2. 

Suppose further that you want to introduce a common correlation among the observations from a 

single individual, with correlation being the same for all individuals. One way of setting this up in 

the general mixed model is to eliminate the Z and G matrices and let the R matrix be block diagonal 

with blocks corresponding to the individuals and with each block having the compound-symmetry 

structure. This structure has two unknown parameters, one modeling a common covariance and the 

other modeling a residual variance. The form for R would then be as follows: 

2 

6 

R D 6 

4 

2 

1 C 2 2 2 

1 

1 

2 2 

1 1 C 2 2 1 

2 

2 2 

1 

1 1 

C 2 

: :: 

2 

1 C 2 2 2 

1 

1 

2 2 

1 1 C 2 2 1 

2 

2 2 

1 

1 1 

C 2 

where blanks denote zeros. There are 3s rows and columns altogether, and the common correlation 

is 2 1 =. 2 1 C 2 /. 

The PROC MIXED statements to fit this model are as follows: 

3 

7 

5

proc mixed; 

class indiv; 


repeated / type=cs subject=indiv; 

run; 


Here, indiv is a classification variable indexing individuals. The MODEL statement fits a straight line 

for time ; the intercept is fit by default just as in PROC GLM. The REPEATED statement models the 

R matrix: TYPE=CS specifies the compound symmetry structure, and SUBJECT=INDIV specifies 

the blocks of R. 

An alternative way of specifying the common intra-individual correlation is to let 

2 

6 

Z D 6 

4 

2 

6 

G D 6 

4 

1 

1 

1 

2 

1 

1 

1 

1 

2 

1 

: :: 

: :: 

1 

1 

1 

3 

7 

5 

2 

1 

3 

7 

5 

and R D 2 In. The Z matrix has 3s rows and s columns, and G is s s. 

You can set up this model in PROC MIXED in two different but equivalent ways: 

proc mixed; 

class indiv; 


random indiv; 

run; 

proc mixed; 

class indiv; 


random intercept / subject=indiv; 

run; 

Both of these specifications fit the same model as the previous one that used the REPEATED statement; 

however, the RANDOM specifications constrain the correlation to be positive, whereas the 

REPEATED specification leaves the correlation unconstrained.


Example: Split-Plot Design 

The split-plot design involves two experimental treatment factors, A and B, and two different sizes of 

experimental units to which they are applied (see Winer 1971, Snedecor and Cochran 1980, Milliken 

and Johnson 1992, and Steel, Torrie, and Dickey 1997). The levels of A are randomly assigned to 

the larger-sized experimental unit, called whole plots, whereas the levels of B are assigned to the 

smaller-sized experimental unit, the subplots. The subplots are assumed to be nested within the 

whole plots, so that a whole plot consists of a cluster of subplots and a level of A is applied to the 

entire cluster. 

Such an arrangement is often necessary by nature of the experiment, the classical example being 

the application of fertilizer to large plots of land and different crop varieties planted in subdivisions 

of the large plots. For this example, fertilizer is the whole-plot factor A and variety is the subplot 

factor B. 

The first example is a split-plot design for which the whole plots are arranged in a randomized block 

design. The appropriate PROC MIXED statements are as follows: 

Here 

proc mixed; 

class a b block; 

model y = a|b; 

random block a*block; 

run; 

R D 2 I24 

and X, Z, and G have the following form: 

2 

1 1 1 1 

3 

6 1 

6 

1 

6 

1 

6 1 

6 1 

6 

X D 6 : 

6 1 

6 1 

6 1 

6 1 

4 1 

1 

1 

1 

1 

1 

: 

1 

1 

1 

1 

1 

1 

1 

: 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

: 

1 

1 

1 

7 

1 7 

5 

1 1 1 1

2 

6 

Z D 6 

4 

2 

6 

G D 6 

4 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

2 

B 

2 

B 

2 

B 

2 

B 

2 

AB 

2 

AB 

: :: 

2 

AB 

3 

7 

5 


where 2 B is the variance component for Block and 2 AB is the variance component for A*Block. 

Changing the RANDOM statement as follows fits the same model, but with Z and G sorted differently: 

random int a / subject=block; 

3 

7 

5


2 

6 

Z D 6 

4 

2 

6 

G D 6 

4 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

2 

B 

2 

AB 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

2 

AB 

2 

AB 

: :: 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

2 

B 

2 

AB 

Estimating Covariance Parameters in the Mixed Model 

1 1 

1 1 

1 1 

1 1 

1 1 

1 1 

Estimation is more difficult in the mixed model than in the general linear model. Not only do you 

have ˇ as in the general linear model, but you have unknown parameters in , G, and R as well. 

Least squares is no longer the best method. Generalized least squares (GLS) is more appropriate, 

minimizing 

.y Xˇ/ 0 V 1 .y Xˇ/ 

However, it requires knowledge of V and, therefore, knowledge of G and R. Lacking such information, 

one approach is to use estimated GLS, in which you insert some reasonable estimate for V 

into the minimization problem. The goal thus becomes finding a reasonable estimate of G and R. 

2 

AB 

2 

AB 

3 

7 

5 

3 

7 

5


In many situations, the best approach is to use likelihood-based methods, exploiting the assumption 

that and are normally distributed (Hartley and Rao 1967; Patterson and Thompson 1971; 

Harville 1977; Laird and Ware 1982; Jennrich and Schluchter 1986). PROC MIXED implements 

two likelihood-based methods: maximum likelihood (ML) and restricted/residual maximum likelihood 

(REML). A favorable theoretical property of ML and REML is that they accommodate data 

that are missing at random (Rubin 1976; Little 1995). 

PROC MIXED constructs an objective function associated with ML or REML and maximizes it 

over all unknown parameters. Using calculus, it is possible to reduce this maximization problem 

to one over only the parameters in G and R. The corresponding log-likelihood functions are as 

follows: 

ML W l.G; R/ D 1 

log jVj 

2 

REML W lR.G; R/ D 1 

log jVj 

2 

1 

2 r0 V 1 r 

1 

2 log jX0 V 1 Xj 

n 

log.2 / 

2 

1 

2 r0 V 1 r 

n p 

2 

log.2 /g 

where r D y X.X 0 V 1 X/ X 0 V 1 y and p is the rank of X. PROC MIXED actually minimizes 

2 times these functions by using a ridge-stabilized Newton-Raphson algorithm. Lindstrom and 

Bates (1988) provide reasons for preferring Newton-Raphson to the Expectation-Maximum (EM) 

algorithm described in Dempster, Laird, and Rubin (1977) and Laird, Lange, and Stram (1987), as 

well as analytical details for implementing a QR-decomposition approach to the problem. Wolfinger, 

Tobias, and Sall (1994) present the sweep-based algorithms that are implemented in PROC 

MIXED. 

One advantage of using the Newton-Raphson algorithm is that the second derivative matrix of the 

objective function evaluated at the optima is available upon completion. Denoting this matrix H, 

the asymptotic theory of maximum likelihood (see Serfling 1980) shows that 2H 1 is an asymptotic 

variance-covariance matrix of the estimated parameters of G and R. Thus, tests and confidence 

intervals based on asymptotic normality can be obtained. However, these can be unreliable in small 

samples, especially for parameters such as variance components that have sampling distributions 

that tend to be skewed to the right. 

If a residual variance 2 is a part of your mixed model, it can usually be profiled out of the likelihood. 

This means solving analytically for the optimal 2 and plugging this expression back into the 

likelihood formula (see Wolfinger, Tobias, and Sall 1994). This reduces the number of optimization 

parameters by one and can improve convergence properties. PROC MIXED profiles the residual 

variance out of the log likelihood whenever it appears reasonable to do so. This includes the case 

when R equals 2 I and when it has blocks with a compound symmetry, time series, or spatial structure. 

PROC MIXED does not profile the log likelihood when R has unstructured blocks, when you 

use the HOLD= or NOITER option in the PARMS statement, or when you use the NOPROFILE 

option in the PROC MIXED statement. 

Instead of ML or REML, you can use the noniterative MIVQUE0 method to estimate G and R (Rao 

1972; LaMotte 1973; Wolfinger, Tobias, and Sall 1994). In fact, by default PROC MIXED uses 

MIVQUE0 estimates as starting values for the ML and REML procedures. For variance component 

models, another estimation method involves equating Type 1, 2, or 3 expected mean squares to 

their observed values and solving the resulting system. However, Swallow and Monahan (1984) 

present simulation evidence favoring REML and ML over MIVQUE0 and other method-of-moment 

estimators.


Estimating Fixed and Random Effects in the Mixed Model 

ML, REML, MIVQUE0, or Type1–Type3 provide estimates of G and R, which are denoted bG and 

bR, respectively. To obtain estimates of ˇ and , the standard method is to solve the mixed model 

equations (Henderson 1984): 

" 

X 0bR 1 X X 0bR 1 Z 

Z 0bR 1 X Z 0bR 1 Z C bG 1 

The solutions can also be written as 

bˇ D .X 0bV 1 X/ X 0bV 1 y 

b D bGZ 0bV 1 .y Xbˇ/ 

# 

bˇ 

b D 

" 

X 0bR 1 y 

Z 0bR 1 y 

and have connections with empirical Bayes estimators (Laird and Ware 1982, Carlin and Louis 

1996). 

Note that the mixed model equations are extended normal equations and that the preceding expression 

assumes that bG is nonsingular. For the extreme case where the eigenvalues of bG are very large, 

bG 1 contributes very little to the equations and b is close to what it would be if actually contained 

fixed-effects parameters. On the other hand, when the eigenvalues of bG are very small, bG 1 dominates 

the equations and b is close to 0. For intermediate cases, bG 1 can be viewed as shrinking the 

fixed-effects estimates of toward 0 (Robinson 1991). 

If bG is singular, then the mixed model equations are modified (Henderson 1984) as follows: 

" 

X 0bR 1X X 0bR 1 bL 

ZbL 

0Z 0bR 1X bL 0Z 0bR 1 # 

ZbL C I 

bˇ 

b D 

" 

X 0bR 1y bL 0Z 0bR 1 # 

y 

where bL is the lower-triangular Cholesky root of bG, satisfying bG D bLbL 0 . Both b and a generalized 

inverse of the left-hand-side coefficient matrix are then transformed by using bL to determine b. 

An example of when the singular form of the equations is necessary is when a variance component 

estimate falls on the boundary constraint of 0. 

Model Selection 

The previous section on estimation assumes the specification of a mixed model in terms of X, Z, 

G, and R. Even though X and Z have known elements, their specific form and construction are 

flexible, and several possibilities can present themselves for a particular data set. Likewise, several 

different covariance structures for G and R might be reasonable. 

Space does not permit a thorough discussion of model selection, but a few brief comments and 

references are in order. First, subject matter considerations and objectives are of great importance 

when selecting a model; see Diggle (1988) and Lindsey (1993). 

Second, when the data themselves are looked to for guidance, many of the graphical methods and 

diagnostics appropriate for the general linear model extend to the mixed model setting as well 

(Christensen, Pearson, and Johnson 1992). 

#


Finally, a likelihood-based approach to the mixed model provides several statistical measures for 

model adequacy as well. The most common of these are the likelihood ratio test and Akaike’s and 

Schwarz’s criteria (Bozdogan 1987; Wolfinger 1993; Keselman et al. 1998, 1999). 

Statistical Properties 

If G and R are known, bˇ is the best linear unbiased estimator (BLUE) of ˇ, and b is the best linear 

unbiased predictor (BLUP) of (Searle 1971; Harville 1988, 1990; Robinson 1991; McLean, 

Sanders, and Stroup 1991). Here, “best” means minimum mean squared error. The covariance 

matrix of .bˇ ˇ; b / is 

C D X0 R 1 X X 0 R 1 Z 

Z 0 R 1 X Z 0 R 1 Z C G 1 

where denotes a generalized inverse (see Searle 1971). 

However, G and R are usually unknown and are estimated by using one of the aforementioned 

methods. These estimates, bG and bR, are therefore simply substituted into the preceding expression 

to obtain 

bC D 

" 

X 0bR 1 X X 0bR 1 Z 

Z 0bR 1 X Z 0bR 1 Z C bG 1 

# 

as the approximate variance-covariance matrix of .bˇ ˇ; b ). In this case, the BLUE and BLUP 

acronyms no longer apply, but the word empirical is often added to indicate such an approximation. 

The appropriate acronyms thus become EBLUE and EBLUP. 

McLean and Sanders (1988) show that bC can also be written as 

" 

bC11 bC D 

bC 0 21 

# 

where 

bC21 bC22 

bC11 D .X 0bV 1 X/ 

bC21 D bGZ 0bV 1 XbC11 

bC22 D .Z 0bR 1 Z C bG 1 / 1 bC21X 0bV 1 ZbG 

Note that bC11 is the familiar estimated generalized least squares formula for the variance-covariance 

matrix of bˇ. 

As a cautionary note, bC tends to underestimate the true sampling variability of 

(bˇ b) because no account is made for the uncertainty in estimating G and R. Although inflation 

factors have been proposed (Kackar and Harville 1984; Kass and Steffey 1989; Prasad and 

Rao 1990), they tend to be small for data sets that are fairly well balanced. PROC MIXED does not 

compute any inflation factors by default, but rather accounts for the downward bias by using the 

approximate t and F statistics described subsequently. The DDFM=KENWARDROGER option 

in the MODEL statement prompts PROC MIXED to compute a specific inflation factor along with 

Satterthwaite-based degrees of freedom.


Inference and Test Statistics 

For inferences concerning the covariance parameters in your model, you can use likelihood-based 

statistics. One common likelihood-based statistic is the Wald Z, which is computed as the parameter 

estimate divided by its asymptotic standard error. The asymptotic standard errors are computed from 

the inverse of the second derivative matrix of the likelihood with respect to each of the covariance 

parameters. The Wald Z is valid for large samples, but it can be unreliable for small data sets 

and for parameters such as variance components, which are known to have a skewed or bounded 

sampling distribution. 

A better alternative is the likelihood ratio 2 statistic. This statistic compares two covariance models, 

one a special case of the other. To compute it, you must run PROC MIXED twice, once for 

each of the two models, and then subtract the corresponding values of 2 times the log likelihoods. 

You can use either ML or REML to construct this statistic, which tests whether the full model is 

necessary beyond the reduced model. 

As long as the reduced model does not occur on the boundary of the covariance parameter space, 

the 2 statistic computed in this fashion has a large-sample 2 distribution that is 2 with degrees 

of freedom equal to the difference in the number of covariance parameters between the two models. 

If the reduced model does occur on the boundary of the covariance parameter space, the asymptotic 

distribution becomes a mixture of 2 distributions (Self and Liang 1987). A common example of 

this is when you are testing that a variance component equals its lower boundary constraint of 0. 

A final possibility for obtaining inferences concerning the covariance parameters is to simulate or 

resample data from your model and construct empirical sampling distributions of the parameters. 

The SAS macro language and the ODS system are useful tools in this regard. 

F and t Tests for Fixed- and Random-Effects Parameters 

For inferences concerning the fixed- and random-effects parameters in the mixed model, consider 

estimable linear combinations of the following form: 

L ˇ 

The estimability requirement (Searle 1971) applies only to the ˇ portion of L, because any linear 

combination of is estimable. Such a formulation in terms of a general L matrix encompasses a 

wide variety of common inferential procedures such as those employed with Type 1–Type 3 tests 

and LS-means. The CONTRAST and ESTIMATE statements in PROC MIXED enable you to 

specify your own L matrices. Typically, inference on fixed effects is the focus, and, in this case, the 

portion of L is assumed to contain all 0s. 

Statistical inferences are obtained by testing the hypothesis 

H W L ˇ D 0 

or by constructing point and interval estimates.


When L consists of a single row, a general t statistic can be constructed as follows (see McLean and 

Sanders 1988, Stroup 1989a): 

t D 

L bˇ 

b 

p 

LbCL 0 

Under the assumed normality of and , t has an exact t distribution only for data exhibiting 

certain types of balance and for some special unbalanced cases. In general, t is only approximately 

t-distributed, and its degrees of freedom must be estimated. See the DDFM= option for a description 

of the various degrees-of-freedom methods available in PROC MIXED. 

With b being the approximate degrees of freedom, the associated confidence interval is 

L bˇ 

b ˙ t q 

LbCL b;˛=2 

0 

where t b;˛=2 is the .1 ˛=2/100th percentile of the t b distribution. 

When the rank of L is greater than 1, PROC MIXED constructs the following general F statistic: 

F D 

bˇ 

b 

0 

L 0 .LbCL 0 / 1 L bˇ 

b 

r 

where r D rank.LbCL 0 /. Analogous to t, F in general has an approximate F distribution with r 

numerator degrees of freedom and b denominator degrees of freedom. 

The t and F statistics enable you to make inferences about your fixed effects, which account for 

the variance-covariance model you select. An alternative is the 2 statistic associated with the 

likelihood ratio test. This statistic compares two fixed-effects models, one a special case of the 

other. It is computed just as when comparing different covariance models, although you should use 

ML and not REML here because the penalty term associated with restricted likelihoods depends 

upon the fixed-effects specification. 

F Tests With the ANOVAF Option 

The ANOVAF option computes F tests by the following method in models with REPEATED statement 

and without RANDOM statement. Let L denote the matrix of estimable functions for the 

hypothesis H W Lˇ D 0, where ˇ are the fixed-effects parameters. Let M D L 0 .LL 0 / L, and 

suppose that bC denotes the estimated variance-covariance matrix of bˇ (see the section “Statistical 

Properties” for the construction of bC). 

The ANOVAF F statistics are computed as 

FA D bˇ 0 L 0 LL 0 . 

1 

Lbˇ t1 D bˇ 0 . 

Mbˇ 

t1 

Notice that this is a modification of the usual F statistic where .LbCL 0 / 1 is replaced with .LL 0 / 1 

and rank.L/ is replaced with t1 D trace.MbC/; see, for example, Brunner, Domhof, and Langer


(2002, Sec. 5.4). The p-values for this statistic are computed from either an F 1; 2 or an F 1;1 

distribution. The respective degrees of freedom are determined by the MIXED procedure as follows: 

1 D 

t 2 1 

trace.MbCMbC/ 

2 D 2t 2 1 

g 0 Ag 

2 D maxfminf 2 ; dfeg; 1g g 0 Ag > 1E3 MACEPS 

1 otherwise 

The term g 0 Ag in the term 2 for the denominator degrees of freedom is based on approximating 

VarŒtrace.MbC/ based on a first-order Taylor series about the true covariance parameters. This 

generalizes results in the appendix of Brunner, Dette, and Munk (1997) to a broader class of models. 

The vector g D Œg1; ; gq contains the partial derivatives 

trace 

L 0 LL 0 ! 

1 @bC 

L 

@ i 

and A is the asymptotic variance-covariance matrix of the covariance parameter estimates 

(ASYCOV option). 

PROC MIXED reports 1 and 2 as “NumDF” and “DenDF” under the “ANOVA F” heading in the 

output. The corresponding p-values are denoted as “Pr > F(DDF)” for F 1; 2 and “Pr > F(infty)” 

for F 1;1, respectively. 

P-values computed with the ANOVAF option can be identical to the nonparametric tests in Akritas, 

Arnold, and Brunner (1997) and in Brunner, Domhof, and Langer (2002), provided that the 

response data consist of properly created (and sorted) ranks and that the covariance parameters are 

estimated by MIVQUE0 in models with REPEATED statement and properly chosen SUBJECT= 

and/or GROUP= effects. 

If you model an unstructured covariance matrix in a longitudinal model with one or more repeated 

factors, the ANOVAF results are identical to a multivariate MANOVA where degrees of freedom 

are corrected with the Greenhouse-Geiser adjustment (Greenhouse and Geiser 1959). For example, 

suppose that factor A has 2 levels and factor B has 4 levels. The following two sets of statements 

produce the same p-values: 

proc mixed data=Mydata anovaf method=mivque0; 

class id A B; 

model score = A | B / chisq; 

repeated / type=un subject=id; 

ods select Tests3; 

run; 

proc transpose data=MyData out=tdata; 

by id; 

var score; 

proc glm data=tdata; 

model col: = / nouni;

Parameterization of Mixed Models ✦ 3975 

repeated A 2, B 4; 

ods output ModelANOVA=maov epsilons=eps; 

run; 

proc transpose data=eps(where=(substr(statistic,1,3)=’Gre’)) out=teps; 

var cvalue1; 

run; 

data aov; set maov; 

if (_n_ = 1) then merge teps; 

if (Source=’A’) then do; 

pFddf = ProbF; 

pFinf = 1 - probchi(df*Fvalue,df); 

output; 

end; else if (Source=’B’) then do; 

pFddf = ProbFGG; 

pFinf = 1 - probchi(df*col1*Fvalue,df*col1); 

output; 

end; else if (Source=’A*B’) then do; 

pfddF = ProbFGG; 

pFinf = 1 - probchi(df*col2*Fvalue,df*col2); 

output; 

end; 

proc print data=aov label noobs; 

label Source = ’Effect’ 

df = ’NumDF’ 

Fvalue = ’Value’ 

pFddf = ’Pr > F(DDF)’ 

pFinf = ’Pr > F(infty)’; 

var Source df Fvalue pFddf pFinf; 

format pF: pvalue6.; 

run; 

The PROC GLM code produces p-values that correspond to the ANOVAF p-values shown as Pr > 

F(DDF) in the MIXED output. The subsequent DATA step computes the p-values that correspond 

to Pr > F(infty) in the PROC MIXED output. 

Parameterization of Mixed Models 

Recall that a mixed model is of the form 

y D Xˇ C Z C 

where y represents univariate data, ˇ is an unknown vector of fixed effects with known model matrix 

X, is an unknown vector of random effects with known model matrix Z, and is an unknown 

random error vector. 

PROC MIXED constructs a mixed model according to the specifications in the MODEL, 

RANDOM, and REPEATED statements. Each effect in the MODEL statement generates one or 

more columns in the model matrix X, and each effect in the RANDOM statement generates one or 

more columns in the model matrix Z. Effects in the REPEATED statement do not generate model


matrices; they serve only to index observations within subjects. This section shows precisely how 

PROC MIXED builds X and Z. 

Intercept 

By default, all models automatically include a column of 1s in X to estimate a fixed-effect intercept 

parameter . You can use the NOINT option in the MODEL statement to suppress this intercept. 

The NOINT option is useful when you are specifying a classification effect in the MODEL statement 

and you want the parameter estimate to be in terms of the mean response for each level of that effect, 

rather than in terms of a deviation from an overall mean. 

By contrast, the intercept is not included by default in Z. To obtain a column of 1s in Z, you must 

specify in the RANDOM statement either the INTERCEPT effect or some effect that has only one 

level. 

Regression Effects 

Numeric variables, or polynomial terms involving them, can be included in the model as regression 

effects (covariates). The actual values of such terms are included as columns of the model matrices 

X and Z. You can use the bar operator with a regression effect to generate polynomial effects. For 

instance, X|X|X expands to X X*X X*X*X, a cubic model. 

Main Effects 

If a classification variable has m levels, PROC MIXED generates m columns in the model matrix 

for its main effect. Each column is an indicator variable for a given level. The order of the columns 

is the sort order of the values of their levels and can be controlled with the ORDER= option in the 

PROC MIXED statement. Table 56.16 is an example. 

Table 56.16 Example of Main Effects 

Data I A B 

A B A1 A2 B1 B2 B3 

1 1 1 1 0 1 0 0 

1 2 1 1 0 0 1 0 

1 3 1 1 0 0 0 1 

2 1 1 0 1 1 0 0 

2 2 1 0 1 0 1 0 

2 3 1 0 1 0 0 1 

Typically, there are more columns for these effects than there are degrees of freedom for them. In 

other words, PROC MIXED uses an overparameterized model.

Interaction Effects 


Often a model includes interaction (crossed) effects. With an interaction, PROC MIXED first reorders 

the terms to correspond to the order of the variables in the CLASS statement. Thus, B*A 

becomes A*B if A precedes B in the CLASS statement. Then, PROC MIXED generates columns for 

all combinations of levels that occur in the data. The order of the columns is such that the rightmost 

variables in the cross index faster than the leftmost variables (Table 56.17). Empty columns (that 

would contain all 0s) are not generated for X, but they are for Z. 

Table 56.17 Example of Interaction Effects 

Data I A B A*B 

A B A1 A2 B1 B2 B3 A1B1 A1B2 A1B3 A2B1 A2B2 A2B3 

1 1 1 1 0 1 0 0 1 0 0 0 0 0 

1 2 1 1 0 0 1 0 0 1 0 0 0 0 

1 3 1 1 0 0 0 1 0 0 1 0 0 0 

2 1 1 0 1 1 0 0 0 0 0 1 0 0 

2 2 1 0 1 0 1 0 0 0 0 0 1 0 

2 3 1 0 1 0 0 1 0 0 0 0 0 1 

In the preceding matrix, main-effects columns are not linearly independent of crossed-effects 

columns; in fact, the column space for the crossed effects contains the space of the main effect. 

When your model contains many interaction effects, you might be able to code them more parsimoniously 

by using the bar operator ( | ). The bar operator generates all possible interaction effects. 

For example, A|B|C expands to A B A*B C A*C B*C A*B*C. To eliminate higher-order interaction 

effects, use the at sign (@) in conjunction with the bar operator. For instance, A|B|C|D @2 expands 

to A B A*B C A*C B*C D A*D B*D C*D. 

Nested Effects 

Nested effects are generated in the same manner as crossed effects. Hence, the design columns generated 

by the following two statements are the same (but the ordering of the columns is different): 

model Y=A B(A); 

model Y=A A*B; 

The nesting operator in PROC MIXED is more a notational convenience than an operation distinct 

from crossing. Nested effects are typically characterized by the property that the nested variables 

never appear as main effects. The order of the variables within nesting parentheses is made to 

correspond to the order of these variables in the CLASS statement. The order of the columns is 

such that variables outside the parentheses index faster than those inside the parentheses, and the 

rightmost nested variables index faster than the leftmost variables (Table 56.18).


Table 56.18 Example of Nested Effects 

Data I A B(A) 

A B A1 A2 B1A1 B2A1 B3A1 B1A2 B2A2 B3A2 

1 1 1 1 0 1 0 0 0 0 0 

1 2 1 1 0 0 1 0 0 0 0 

1 3 1 1 0 0 0 1 0 0 0 

2 1 1 0 1 0 0 0 1 0 0 

2 2 1 0 1 0 0 0 0 1 0 

2 3 1 0 1 0 0 0 0 0 1 

Note that nested effects are often distinguished from interaction effects by the implied randomization 

structure of the design. That is, they usually indicate random effects within a fixed-effects 

framework. The fact that random effects can be modeled directly in the RANDOM statement might 

make the specification of nested effects in the MODEL statement unnecessary. 

Continuous-Nesting-Class Effects 

When a continuous variable nests with a classification variable, the design columns are constructed 

by multiplying the continuous values into the design columns for the class effect (Table 56.19). 

Table 56.19 Example of Continuous-Nesting-Class Effects 

Data I A X(A) 

X A A1 A2 X(A1) X(A2) 

21 1 1 1 0 21 0 

24 1 1 1 0 24 0 

22 1 1 1 0 22 0 

28 2 1 0 1 0 28 

19 2 1 0 1 0 19 

23 2 1 0 1 0 23 

This model estimates a separate slope for X within each level of A. 

Continuous-by-Class Effects 

Continuous-by-class effects generate the same design columns as continuous-nesting-class effects. 

The two models are made different by the presence of the continuous variable as a regressor by 

itself, as well as a contributor to a compound effect. Table 56.20 shows an example.

Table 56.20 Example of Continuous-by-Class Effects 

Data I X A X*A 


X A X A1 A2 X*A1 X*A2 

21 1 1 21 1 0 21 0 

24 1 1 24 1 0 24 0 

22 1 1 22 1 0 22 0 

28 2 1 28 0 1 0 28 

19 2 1 19 0 1 0 19 

23 2 1 23 0 1 0 23 

You can use continuous-by-class effects to test for homogeneity of slopes. 

General Effects 

An example that combines all the effects is X1*X2*A*B*C (D E). The continuous list comes first, 

followed by the crossed list, followed by the nested list in parentheses. You should be aware of 

the sequencing of parameters when you use the CONTRAST or ESTIMATE statement to compute 

some function of the parameter estimates. 

Effects might be renamed by PROC MIXED to correspond to ordering rules. For example, B*A(E 

D) might be renamed A*B(D E) to satisfy the following: 

Classification variables that occur outside parentheses (crossed effects) are sorted in the order 

in which they appear in the CLASS statement. 

Variables within parentheses (nested effects) are sorted in the order in which they appear in 

the CLASS statement. 

The sequencing of the parameters generated by an effect can be described by which variables have 

their levels indexed faster: 

Variables in the crossed list index faster than variables in the nested list. 

Within a crossed or nested list, variables to the right index faster than variables to the left. 

For example, suppose a model includes four effects—A, B, C, and D—each having two levels, 1 and 

2. Suppose the CLASS statement is as follows: 

class A B C D; 

Then the order of the parameters for the effect B*A(C D), which is renamed A*B (C D), is 

A1B1C1D1 ! A1B2C1D1 ! A2B1C1D1 ! A2B2C1D1 ! 



A1B1C2D2 ! A1B2C2D2 ! A2B1C2D2 ! A2B2C2D2


Note that first the crossed effects B and A are sorted in the order in which they appear in the CLASS 

statement so that A precedes B in the parameter list. Then, for each combination of the nested effects 

in turn, combinations of A and B appear. The B effect moves fastest because it is rightmost in the 

cross list. Then A moves next fastest, and D moves next fastest. The C effect is the slowest since it 

is leftmost in the nested list. 

When numeric levels are used, levels are sorted by their character format, which might not correspond 

to their numeric sort sequence (for example, noninteger levels). Therefore, it is advisable to 

include a desired format for numeric levels or to use the ORDER=INTERNAL option in the PROC 

MIXED statement to ensure that levels are sorted by their internal values. 

Implications of the Non-Full-Rank Parameterization 

For models with fixed effects involving classification variables, there are more design columns in 

X constructed than there are degrees of freedom for the effect. Thus, there are linear dependencies 

among the columns of X. In this event, all of the parameters are not estimable; there is an infinite 

number of solutions to the mixed model equations. PROC MIXED uses a generalized inverse (a 

g2-inverse, Pringle and Rayner, 1971) to obtain values for the estimates (Searle 1971). The solution 

values are not displayed unless you specify the SOLUTION option in the MODEL statement. The 

solution has the characteristic that estimates are 0 whenever the design column for that parameter 

is a linear combination of previous columns. With this parameterization, hypothesis tests are 

constructed to test linear functions of the parameters that are estimable. 

Some procedures (such as the CATMOD procedure) reparameterize models to full rank by using 

restrictions on the parameters. PROC GLM and PROC MIXED do not reparameterize, making the 

hypotheses that are commonly tested more understandable. See Goodnight (1978) for additional 

reasons for not reparameterizing. 

Missing Level Combinations 

PROC MIXED handles missing level combinations of classification variables similarly to the way 

PROC GLM does. Both procedures delete fixed-effects parameters corresponding to missing levels 

in order to preserve estimability. However, PROC MIXED does not delete missing level combinations 

for random-effects parameters because linear combinations of the random-effects parameters 

are always estimable. These conventions can affect the way you specify your CONTRAST and 

ESTIMATE coefficients. 

Residuals and Influence Diagnostics 

Residual Diagnostics 

Consider a residual vector of the form e D PY, where P is a projection matrix, possibly an oblique 

projector. A typical element ei with variance vi and estimated variancebvi is said to be standardized

as 

ei ei 

p D pvi 

VarŒei 

and studentized as 

ei 

p bvi 

Residuals and Influence Diagnostics ✦ 3981 

External studentization uses an estimate of VarŒei that does not involve the ith observation. Externally 

studentized residuals are often preferred over studentized residuals because they have wellknown 

distributional properties in standard linear models for independent data. 

q 

Residuals that are scaled by the estimated variance of the response, i.e., ei= cVarŒYi, are referred 

to as Pearson-type residuals. 

Marginal and Conditional Residuals 

The marginal and conditional means in the linear mixed model are EŒY D Xˇ and EŒYj D 

Xˇ C Z , respectively. Accordingly, the vector rm of marginal residuals is defined as 

rm D Y Xbˇ 

and the vector rc of conditional residuals is 

rc D Y Xbˇ Zb D rm Zb 

Following Gregoire, Schabenberger, and Barrett (1995), let Q D X.X 0bV 1 X/ X 0 and K D I 

ZbGZ 0bV 1 . Then 

cVarŒrm D bV Q 

cVarŒrc D K.bV Q/K 0 

For an individual observation the raw, studentized, and Pearson-type residuals computed by the 

MIXED procedure are given in Table 56.21. 

Table 56.21 Residual Types Computed by the MIXED Procedure 

Type of Residual Marginal Conditional 

Raw rmi D Yi x 0 i bˇ rci D rmi z 0 i b 

Studentized rstudent mi D rmi p 

bVarŒrmi 

Pearson r pearson 

mi D rmi p 

bVarŒYi 

rstudent ci D rci p 

bVarŒrci 

r pearson 

ci 

D 

rci p 

bVarŒYi j


When the OUTPM= option is specified in addition to the RESIDUAL option in the MODEL statement, 

rstudent mi and r pearson 

mi are added to the data set as variables Resid, StudentResid, and PearsonResid, 

respectively. When the OUTP= option is specified, rstudent ci and r pearson 

ci are added to 

the data set. Raw residuals are part of the OUTPM= and OUTP= data sets without the RESIDUAL 

option. 

Scaled Residuals 

For correlated data, a set of scaled quantities can be defined through the Cholesky decomposition 

of the variance-covariance matrix. Since fitted residuals in linear models are rank-deficient, it is 

customary to draw on the variance-covariance matrix of the data. If VarŒY D V and C 0 C D V, 

then C 0 1 Y has uniform dispersion and its elements are uncorrelated. 

Scaled residuals in a mixed model are meaningful for quantities based on the marginal distribution 

of the data. Let bC denote the Cholesky root of bV, so that bC 0bC D bV, and define 

Yc D bC 0 1 Y 

r m.c/ D bC 0 1 rm 

By analogy with other scalings, the inverse Cholesky decomposition can also be applied to the 

residual vector, bC 0 1 rm, although V is not the variance-covariance matrix of rm. 

To diagnose whether the covariance structure of the model has been specified correctly can be 

difficult based on Yc, since the inverse Cholesky transformation affects the expected value of Yc. 

You can draw on r m.c/ as a vector of (approximately) uncorrelated data with constant mean. 

When the OUTPM= option in the MODEL statement is specified in addition to the VCIRY option, 

Yc is added as variable ScaledDep and r m.c/ is added as ScaledResid to the data set. 

Influence Diagnostics 

Basic Idea and Statistics 

The general idea of quantifying the influence of one or more observations relies on computing parameter 

estimates based on all data points, removing the cases in question from the data, refitting 

the model, and computing statistics based on the change between full-data and reduced-data estimation. 

Influence statistics can be coarsely grouped by the aspect of estimation that is their primary 

target: 

overall measures compare changes in objective functions: (restricted) likelihood distance 

(Cook and Weisberg 1982, Ch. 5.2) 

influence on parameter estimates: Cook’s D (Cook 1977, 1979), MDFFITS (Belsley, Kuh, 

and Welsch 1980, p. 32) 

influence on precision of estimates: CovRatio and CovTrace 

influence on fitted and predicted values: PRESS residual, PRESS statistic (Allen 1974), DF- 

FITS (Belsley, Kuh, and Welsch 1980, p. 15)


outlier properties: internally and externally studentized residuals, leverage 

For linear models for uncorrelated data, it is not necessary to refit the model after removing a 

data point in order to measure the impact of an observation on the model. The change in fixed 

effect estimates, residuals, residual sums of squares, and the variance-covariance matrix of the fixed 

effects can be computed based on the fit to the full data alone. By contrast, in mixed models 

several important complications arise. Data points can affect not only the fixed effects but also the 

covariance parameter estimates on which the fixed-effects estimates depend. Furthermore, closedform 

expressions for computing the change in important model quantities might not be available. 

This section provides background material for the various influence diagnostics available with the 

MIXED procedure. See the section “Mixed Models Theory” on page 3962 for relevant expressions 

and definitions. The parameter vector denotes all unknown parameters in the R and G matrix. 

The observations whose influence is being ascertained are represented by the set U and referred to 

simply as “the observations in U .” The estimate of a parameter vector, such as ˇ, obtained from 

all observations except those in the set U is denoted bˇ .U /. In case of a matrix A, the notation 

A .U / represents the matrix with the rows in U removed; these rows are collected in AU . If A is 

symmetric, then notation A .U / implies removal of rows and columns. The vector YU comprises 

the responses of the data points being removed, and V .U / is the variance-covariance matrix of 

the remaining observations. When k D 1, lowercase notation emphasizes that single points are 

removed, such as A .u/. 

Managing the Covariance Parameters 

An important component of influence diagnostics in the mixed model is the estimated variancecovariance 

matrix V D ZGZ 0 CR. To make the dependence on the vector of covariance parameters 

explicit, write it as V. /. If one parameter, 2 , is profiled or factored out of V, the remaining 

parameters are denoted as . Notice that in a model where G is diagonal and R D 2 I, the 

parameter vector contains the ratios of each variance component and 2 (see Wolfinger, Tobias, 

and Sall 1994). When ITER=0, two scenarios are distinguished: 

1. If the residual variance is not profiled, either because the model does not contain a residual 

variance or because it is part of the Newton-Raphson iterations, then b b. .U / 

2. If the residual variance is profiled, then b b and b .U / 

2 

.U / 6D b2 . Influence statistics 

such as Cook’s D and internally studentized residuals are based on V.b/, whereas externally 

studentized residuals and the DFFITS statistic are based on V.bU / D 2 

.U / V.b /. In a 

random components model with uncorrelated errors, for example, the computation of V.bU / 

involves scaling of bG and bR by the full-data estimate b2 and multiplying the result with the 

reduced-data estimate b2 .U / . 

Certain statistics, such as MDFFITS, CovRatio, and CovTrace, require an estimate of the variance 

of the fixed effects that is based on the reduced number of observations. For example, V.b U / is 

evaluated at the reduced-data parameter estimates but computed for the entire data set. The matrix 

V .U /.b .U //, on the other hand, has rows and columns corresponding to the points in U removed. 

The resulting matrix is evaluated at the delete-case estimates.


When influence analysis is iterative, the entire vector is updated, whether the residual variance 

is profiled or not. The matrices to be distinguished here are V.b/, V.b .U //, and V .U /.b .U //, with 

unambiguous notation. 

Predicted Values, PRESS Residual, and PRESS Statistic 

An unconditional predicted value is byi D x 0 i bˇ, where the vector xi is the ith row of X. The (raw) 

residual is given asbi D yi byi, and the PRESS residual is 

b i.U / D yi x 0 i bˇ .U / 

The PRESS statistic is the sum of the squared PRESS residuals, 

PRESS D X 

i2U 

b 2 

i.U / 

where the sum is over the observations in U . 

If EFFECT=, SIZE=, or KEEP= is not specified, PROC MIXED computes the PRESS residual 

for each observation selected through SELECT= (or all observations if SELECT= is not given). If 

EFFECT=, SIZE=, or KEEP= is specified, the procedure computes PRESS. 

Leverage 

For the general mixed model, leverage can be defined through the projection matrix that results from 

a transformation of the model with the inverse of the Cholesky decomposition of V, or through an 

oblique projector. The MIXED procedure follows the latter path in the computation of influence 

diagnostics. The leverage value reported for the ith observation is the ith diagonal entry of the 

matrix 

H D X.X 0 V.b/ 1 X/ X 0 V.b/ 1 

which is the weight of the observation in contributing to its own predicted value, H D dbY=dY. 

While H is idempotent, it is generally not symmetric and thus not a projection matrix in the narrow 

sense. 

The properties of these leverages are generalizations of the properties in models with diagonal 

variance-covariance matrices. For example, bY D HY, and in a model with intercept and V D 2 I, 

the leverage values 

hii D x 0 i .X0 X/ xi 

are h l ii D 1=n hii 1 D h u ii and P n 

iD1 hii D rank.X/. The lower bound for hii is achieved 

in an intercept-only model, and the upper bound is achieved in a saturated model. The trace of H 

equals the rank of X. 

If ij denotes the element in row i, column j of V 1 , then for a model containing only an intercept 

the diagonal elements of H are 

hii D 

Pn j D1 ij 

Pn Pn iD1 j D1 ij


Because P n 

j D1 ij is a sum of elements in the ith row of the inverse variance-covariance matrix, hii 

can be negative, even if the correlations among data points are nonnegative. In case of a saturated 

model with X D I, hii D 1:0. 

Internally and Externally Studentized Residuals 

See the section “Residual Diagnostics” on page 3980 for the distinction between standardization, 

studentization, and scaling of residuals. Internally studentized marginal and conditional residuals 

are computed with the RESIDUAL option of the MODEL statement. The INFLUENCE option 

computes internally and externally studentized marginal residuals. 

The computation of internally studentized residuals relies on the diagonal entries of V.b/ Q.b/, 

where Q.b/ D X.X 0 V.b/ 1 X/ X 0 . Externally studentized residuals require iterative influence 

analysis or a profiled residual variance. In the former case the studentization is based on V.b U /; in 

the latter case it is based on 2 

.U / V.b /. 

Cook’s D 

Cook’s D statistic is an invariant norm that measures the influence of observations in U on a vector 

of parameter estimates (Cook 1977). In case of the fixed-effects coefficients, let 

ı .U / D bˇ bˇ .U / 

Then the MIXED procedure computes 

D.ˇ/ D ı 0 

.U / cVarŒbˇ ı .U /=rank.X/ 

where cVarŒbˇ is the matrix that results from sweeping .X 0 V.b/ 1 X/ . 

If V is known, Cook’s D can be calibrated according to a chi-square distribution with degrees of 

freedom equal to the rank of X (Christensen, Pearson, and Johnson 1992). For estimated V the 

calibration can be carried out according to an F .rank.X/; n rank.X// distribution. To interpret D 

on a familiar scale, Cook (1979) and Cook and Weisberg (1982, p. 116) refer to the 50th percentile 

of the reference distribution. If D is equal to that percentile, then removing the points in U moves 

the fixed-effects coefficient vector from the center of the confidence region to the 50% confidence 

ellipsoid (Myers 1990, p. 262). 

In the case of iterative influence analysis, the MIXED procedure also computes a D-type statistic 

for the covariance parameters. If is the asymptotic variance-covariance matrix of b, then MIXED 

computes 

D D .b b .U /// 0b 1 .b b .U // 

DFFITS and MDFFITS 

A DFFIT measures the change in predicted values due to removal of data points. If this change is 

standardized by the externally estimated standard error of the predicted value in the full data, the 

DFFITS statistic of Belsley, Kuh, and Welsch (1980, p. 15) results: 

DFFITSi D .byi by i.u//=ese.byi/


The MIXED procedure computes DFFITS when the EFFECT= or SIZE= modifier of the 

INFLUENCE option is not in effect. In general, an external estimate of the estimated standard 

error is used. When ITER > 0, the estimate is 

ese.byi/ D 

q 

x 0 i .X0 V.b .u// X/ 1 xi 

When ITER=0 and 2 is profiled, then 

q 

ese.byi/ D b .u/ 

x 0 i .X0 V.b / 1 X/ xi 

When the EFFECT=, SIZE=, or KEEP= modifier is specified, the MIXED procedure computes a 

multivariate version suitable for the deletion of multiple data points. The statistic, termed MDFFITS 

after the MDFFIT statistic of Belsley, Kuh, and Welsch (1980, p. 32), is closely related to Cook’s 

D. Consider the case V D 2 V. / so that 

VarŒbˇ D 2 .X 0 V. / 1 X/ 

and let fVarŒbˇ .U / be an estimate of VarŒbˇ .U / that does not use the observations in U . The MDF- 

FITS statistic is then computed as 

MDFFITS.ˇ/ D ı 0 

.U / fVarŒbˇ .U / ı .U /=rank.X/ 

If ITER=0 and 2 is profiled, then fVarŒbˇ .U / is obtained by sweeping 

b 2 

.U / .X0 

.U / V .U /.b / X .U // 

The underlying idea is that if were known, then 

.X 0 

.U / V .U /. / 1 X .U // 

would be VarŒbˇ= 2 in a generalized least squares regression with all but the data in U . 

In the case of iterative influence analysis, fVarŒbˇ .U / is evaluated at b .U /. Furthermore, a MDFFITStype 

statistic is then computed for the covariance parameters: 

MDFFITS. / D .b b .U // 0 cVarŒb .U / 1 .b b .U // 

Covariance Ratio and Trace 

These statistics depend on the availability of an external estimate of V, or at least of 2 . Whereas 

Cook’s D and MDFFITS measure the impact of data points on a vector of parameter estimates, the 

covariance-based statistics measure impact on their precision. Following Christensen, Pearson, and 

Johnson (1992), the MIXED procedure computes 

CovTrace.ˇ/ D jtrace. cVarŒbˇ fVarŒbˇ .U // rank.X/j 

CovRatio.ˇ/ D detns. fVarŒbˇ .U // 

detns. cVarŒbˇ/ 

where detns.M/ denotes the determinant of the nonsingular part of matrix M.


In the case of iterative influence analysis these statistics are also computed for the covariance parameter 

estimates. If q denotes the rank of VarŒb, then 

CovTrace. / D jtrace. cVarŒb cVarŒb .U // qj 

CovRatio. / D detns. cVarŒb .U // 

detns. cVarŒb/ 

Likelihood Distances 

The log-likelihood function l and restricted log-likelihood function lR of the linear mixed model 

are given in the section “Estimating Covariance Parameters in the Mixed Model” on page 3968. 

Denote as the collection of all parameters, i.e., the fixed effects ˇ and the covariance parameters 

. Twice the difference between the (restricted) log-likelihood evaluated at the full-data estimates 

b and at the reduced-data estimates b .U / is known as the (restricted) likelihood distance: 

RLD .U / D 2flR.b / lR.b .U //g 

LD .U / D 2fl.b / l.b .U //g 

Cook and Weisberg (1982, Ch. 5.2) refer to these differences as likelihood distances, Beckman, 

Nachtsheim, and Cook (1987) call the measures likelihood displacements. If the number of elements 

in that are subject to updating following point removal is q, then likelihood displacements can be 

compared against cutoffs from a chi-square distribution with q degrees of freedom. Notice that this 

reference distribution does not depend on the number of observations removed from the analysis, 

but rather on the number of model parameters that are updated. The likelihood displacement gives 

twice the amount by which the log likelihood of the full data changes if one were to use an estimate 

based on fewer data points. It is thus a global, summary measure of the influence of the observations 

in U jointly on all parameters. 

Unless METHOD=ML, the MIXED procedure computes the likelihood displacement based on the 

residual (=restricted) log likelihood, even if METHOD=MIVQUE0 or METHOD=TYPE1, TYPE2, 

or TYPE3. 

Noniterative Update Formulas 

Update formulas that do not require refitting of the model are available for the cases where V D 

2 I, V is known, or V is known. When ITER=0 and these update formulas can be invoked, the 

MIXED procedure uses the computational devices that are outlined in the following paragraphs. It 

is then assumed that the variance-covariance matrix of the fixed effects has the form .X 0 V 1 X/ . 

When DDFM=KENWARDROGER, this is not the case; the estimated variance-covariance matrix 

is then inflated to better represent the uncertainty in the estimated covariance parameters. Influence 

statistics when DDFM=KENWARDROGER should iteratively update the covariance parameters 

(ITER > 0). The dependence of V on is suppressed in the sequel for brevity. 

Updating the Fixed Effects Denote by U the .n k/ matrix that is assembled from k columns 

of the identity matrix. Each column of U corresponds to the removal of one data point. The point 

being targeted by the ith column of U corresponds to the row in which a 1 appears. Furthermore,


define 

D .X 0 V 1 X/ 

Q D X X 0 

P D V 1 .V Q/V 1 

The change in the fixed-effects estimates following removal of the observations in U is 

bˇ bˇ .U / D X 0 V 1 U.U 0 PU/ 1 U 0 V 1 .y Xbˇ/ 

Using results in Cook and Weisberg (1982, A2) you can further compute 

e D .X 0 1 

.U / V.U / X .U // D C X 0 V 1 U.U 0 PU/ 1 U 0 V 1 X 

If X is .n p/ of rank m < p, then is deficient in rank and the MIXED procedure computes 

needed quantities in e by sweeping (Goodnight 1979). If the rank of the .k k/ matrix U0PU is 

less than k, the removal of the observations introduces a new singularity, whether X is of full rank or 

not. The solution vectors bˇ and bˇ .U / then do not have the same expected values and should not be 

compared. When the MIXED procedure encounters this situation, influence diagnostics that depend 

on the choice of generalized inverse are not computed. The procedure also monitors the singularity 

criteria when sweeping the rows of .X0V 1X/ and of .X0 1 V .U / .U / X .U // . If a new singularity is 

encountered or a former singularity disappears, no influence statistics are computed. 

Residual Variance When 2 is profiled out of the marginal variance-covariance matrix, a closedform 

estimate of 2 that is based on only the remaining observations can be computed provided 

V D V.b / is known. Hurtado (1993, Thm. 5.2) shows that 

.n q r/b 2 

.U / D .n q/b2 b 0 U .b2 U 0 PU/ 1 bU 

and bU D U 0 V 1 .y Xbˇ/. In the case of maximum likelihood estimation q D 0 and for REML 

estimation q D rank.X/. The constant r equals the rank of .U 0 PU/ for REML estimation and the 

number of effective observations that are removed if METHOD=ML. 

Likelihood Distances For noniterative methods the following computational devices are used to 

compute (restricted) likelihood distances provided that the residual variance 2 is profiled. 

The log likelihood function l.b/ evaluated at the full-data and reduced-data estimates can be written 

as 

l.b / D n 

2 log.b2 / 

1 

log jV j 

2 

1 

2 .y Xbˇ/ 0 V 1 .y Xbˇ/=b 2 n 

log.2 / 

2 

l.b 

.U // D n 

2 log.b2 .U / / 

1 

log jV j 

2 

1 

2 .y Xbˇ .U // 0 V 1 .y Xbˇ .U //=b 2 

.U / 

n 

log.2 / 

2 

Notice that l.b .U // evaluates the log likelihood for n data points at the reduced-data estimates. It is 

not the log likelihood obtained by fitting the model to the reduced data. The likelihood distance is 

then 

LD .U / D n log 

( 

2 b.U / 

b2 ) 

n C y Xbˇ .U / 

0 

V 1 y Xbˇ .U / =b 2 

.U / 

Expressions for RLD .U / in noniterative influence analysis are derived along the same lines.

Default Output 

Default Output ✦ 3989 

The following sections describe the output PROC MIXED produces by default. This output is 

organized into various tables, and they are discussed in order of appearance. 


The “Model Information” table describes the model, some of the variables it involves, and the 

method used in fitting it. It also lists the method (profile, factor, parameter, or none) for handling 

the residual variance in the model. The profile method concentrates the residual variance out of the 

optimization problem, whereas the parameter method retains it as a parameter in the optimization. 

The factor method keeps the residual fixed, and none is displayed when a residual variance is not 

part of the model. 

The “Model Information” table also has a row labeled Fixed Effects SE Method. This row describes 

the method used to compute the approximate standard errors for the fixed-effects parameter 

estimates and related functions of them. The two possibilities for this row are Model-Based, which 

is the default method, and Empirical, which results from using the EMPIRICAL option in the PROC 

MIXED statement. 

For ODS purposes, the name of the “Model Information” table is “ModelInfo.” 


The “Class Level Information” table lists the levels of every variable specified in the CLASS statement. 

You should check this information to make sure the data are correct. You can adjust the order 

of the CLASS variable levels with the ORDER= option in the PROC MIXED statement. For ODS 

purposes, the name of the “Class Level Information” table is “ClassLevels.” 

Dimensions 

The “Dimensions” table lists the sizes of relevant matrices. This table can be useful in determining 

CPU time and memory requirements. For ODS purposes, the name of the “Dimensions” table is 

“Dimensions.” 


The “Number of Observations” table shows the number of observations read from the data set and 

the number of observations used in fitting the model.



The “Iteration History” table describes the optimization of the residual log likelihood or log likelihood. 

The function to be minimized (the objective function) is 2l for ML and 2lR for REML; 

the column name of the objective function in the “Iteration History” table is “-2 Log Like” for 

ML and “-2 Res Log Like” for REML. The minimization is performed by using a ridge-stabilized 

Newton-Raphson algorithm, and the rows of this table describe the iterations that this algorithm 

takes in order to minimize the objective function. 

The Evaluations column of the “Iteration History” table tells how many times the objective function 

is evaluated during each iteration. 

The Criterion column of the “Iteration History” table is, by default, a relative Hessian convergence 

quantity given by 

g0 1 H k k gk jfkj where f k is the value of the objective function at iteration k, g k is the gradient (first derivative) of 

f k, and H k is the Hessian (second derivative) of f k. If H k is singular, then PROC MIXED uses the 

following relative quantity: 

g 0 

k g k 

jf kj 

To prevent the division by jf kj, use the ABSOLUTE option in the PROC MIXED statement. To 

use a relative function or gradient criterion, use the CONVF or CONVG option, respectively. 

The Hessian criterion is considered superior to function and gradient criteria because it measures 

orthogonality rather than lack of progress (Bates and Watts 1988). Provided the initial estimate is 

feasible and the maximum number of iterations is not exceeded, the Newton-Raphson algorithm 

is considered to have converged when the criterion is less than the tolerance specified with the 

CONVF, CONVG, or CONVH option in the PROC MIXED statement. The default tolerance is 

1E 8. If convergence is not achieved, PROC MIXED displays the estimates of the parameters at 

the last iteration. 

A convergence criterion that is missing indicates that a boundary constraint has been dropped; it is 

usually not a cause for concern. 

If you specify the ITDETAILS option in the PROC MIXED statement, then the covariance parameter 

estimates at each iteration are included as additional columns in the “Iteration History” table. 

For ODS purposes, the name of the “Iteration History” table is “IterHistory.” 

Convergence Status 

The “Convergence Status” table informs about the status of the iterative estimation process at the 

end of the Newton-Raphson optimization. It appears as a message in the listing, and this message 

is repeated in the log. The ODS object “ConvergenceStatus” also contains several nonprinting 

columns that can be helpful in checking the success of the iterative process, in particular during

Default Output ✦ 3991 

batch processing or when analyzing BY groups. The Status variable takes on the value 0 for a 

successful convergence (even if the Hessian matrix might not be positive definite). The values 1 

and 2 of the Status variable indicate lack of convergence and infeasible initial parameter values, 

respectively. The variables pdG and pdH can be used to check whether the G and R matrices are 

positive definite. 

For models that are not fit iteratively, such as models without random effects or when the NOITER 

option is in effect, the “Convergence Status” is not produced. 

Covariance Parameter Estimates 

The “Covariance Parameter Estimates” table contains the estimates of the parameters in G and 

R (see the section “Estimating Covariance Parameters in the Mixed Model” on page 3968). Their 

values are labeled in the table along with Subject and Group information if applicable. The estimates 

are displayed in the Estimate column and are the results of one of the following estimation methods: 

REML, ML, MIVQUE0, SSCP, Type1, Type2, or Type3. 

If you specify the RATIO option in the PROC MIXED statement, the Ratio column is added to the 

table listing the ratio of each parameter estimate to that of the residual variance. 

Specifying the COVTEST option in the PROC MIXED statement produces the “Std Error,” “Z 

Value,” and “Pr Z” columns. The “Std Error” column contains the approximate standard errors of 

the covariance parameter estimates. These are the square roots of the diagonal elements of the observed 

inverse Fisher information matrix, which equals 2H 1 , where H is the Hessian matrix. The 

H matrix consists of the second derivatives of the objective function with respect to the covariance 

parameters; see Wolfinger, Tobias, and Sall (1994) for formulas. When you use the SCORING= 

option and PROC MIXED converges without stopping the scoring algorithm, PROC MIXED uses 

the expected Hessian matrix to compute the covariance matrix instead of the observed Hessian. The 

observed or expected inverse Fisher information matrix can be viewed as an asymptotic covariance 

matrix of the estimates. 

The “Z Value” column is the estimate divided by its approximate standard error, and the “Pr Z” 

column is the one- or two-tailed area of the standard Gaussian density outside of the Z-value. The 

MIXED procedure computes one-sided p-values for the residual variance and for covariance parameters 

with a lower bound of 0. The procedure computes two-sided p-values otherwise. These 

statistics constitute Wald tests of the covariance parameters, and they are valid only asymptotically. 

CAUTION: Wald tests can be unreliable in small samples. 

For ODS purposes, the name of the “Covariance Parameter Estimates” table is “CovParms.” 


The “Fit Statistics” table provides some statistics about the estimated mixed model. Expressions 

for the 2 times the log likelihood are provided in the section “Estimating Covariance Parameters 

in the Mixed Model” on page 3968. If the log likelihood is an extremely large number, then PROC 

MIXED has deemed the estimated V matrix to be singular. In this case, all subsequent results should 

be viewed with caution.


In addition, the “Fit Statistics” table lists three information criteria: AIC, AICC, and BIC, all in 

smaller-is-better form. Expressions for these criteria are described under the IC option. 

For ODS purposes, the name of the “Model Fitting Information” table is “FitStatistics.” 

Null Model Likelihood Ratio Test 

If one covariance model is a submodel of another, you can carry out a likelihood ratio test for the 

significance of the more general model by computing 2 times the difference between their log 

likelihoods. Then compare this statistic to the 2 distribution with degrees of freedom equal to the 

difference in the number of parameters for the two models. 

This test is reported in the “Null Model Likelihood Ratio Test” table to determine whether it is 

necessary to model the covariance structure of the data at all. The “Chi-Square” value is 2 times 

the log likelihood from the null model minus 2 times the log likelihood from the fitted model, 

where the null model is the one with only the fixed effects listed in the MODEL statement and 

R D 2 I. This statistic has an asymptotic 2 distribution with q 1 degrees of freedom, where q is 

the effective number of covariance parameters (those not estimated to be on a boundary constraint). 

The “Pr > ChiSq” column contains the upper-tail area from this distribution. This p-value can be 

used to assess the significance of the model fit. 

This test is not produced for cases where the null hypothesis lies on the boundary of the parameter 

space, which is typically for variance component models. This is because the standard asymptotic 

theory does not apply in this case (Self and Liang 1987, Case 5). 

If you specify a PARMS statement, PROC MIXED constructs a likelihood ratio test between the 

best model from the grid search and the final fitted model and reports the results in the “Parameter 

Search” table. 

For ODS purposes, the name of the “Null Model Likelihood Ratio Test” table is “LRT.” 


The “Type 3 Tests of Fixed Effects” table contains hypothesis tests for the significance of each of 

the fixed effects—that is, those effects you specify in the MODEL statement. By default, PROC 

MIXED computes these tests by first constructing a Type 3 L matrix (see Chapter 15, “The Four 

Types of Estimable Functions”) for each effect. This L matrix is then used to compute the following 

F statistic: 

F D bˇ 0 L 0 ŒL.X 0bV 1 X/ L 0 Lbˇ 

r 

where r D rank.L.X 0bV 1 X/ L 0 /. A p-value for the test is computed as the tail area beyond this 

statistic from an F distribution with NDF and DDF degrees of freedom. The numerator degrees 

of freedom (NDF) are the row rank of L, and the denominator degrees of freedom are computed 

by using one of the methods described under the DDFM= option. Small values of the p-value 

(typically less than 0.05 or 0.01) indicate a significant effect. 

You can use the HTYPE= option in the MODEL statement to obtain tables of Type 1 (sequential) 

tests and Type 2 (adjusted) tests in addition to or instead of the table of Type 3 (partial) tests.

ODS Table Names ✦ 3993 

You can use the CHISQ option in the MODEL statement to obtain Wald 2 tests of the fixed 

effects. These are carried out by using the numerator of the F statistic and comparing it with the 2 

distribution with NDF degrees of freedom. It is more liberal than the F test because it effectively 

assumes infinite denominator degrees of freedom. 

For ODS purposes, the names of the “Type 1 Tests of Fixed Effects” through the “Type 3 Tests of 

Fixed Effects” tables are “Tests1” through “Tests3,” respectively. 

ODS Table Names 

Each table created by PROC MIXED has a name associated with it, and you must use this name to 

reference the table when using ODS statements. These names are listed in Table 56.22. 

Table 56.22 ODS Tables Produced by PROC MIXED 

Table Name Description Required Statement / Option 

AccRates acceptance rates for posterior sampling 

PRIOR 

AsyCorr asymptotic correlation matrix of 

covariance parameters 

PROC MIXED ASYCORR 

AsyCov asymptotic covariance matrix of 


PROC MIXED ASYCOV 

Base base densities used for posterior 

sampling 

PRIOR 

Bound computed bound for posterior rejection 

sampling 

PRIOR 

CholG Cholesky root of the estimated G 

matrix 

RANDOM / GC 

CholR Cholesky root of blocks of the estimated 

R matrix 

REPEATED / RC 

CholV Cholesky root of blocks of the estimated 

V matrix 

RANDOM / VC 

ClassLevels level information from the CLASS 

statement 

default output 

Coef L matrix coefficients E option in MODEL, 

CONTRAST, ESTIMATE, 

or LSMEANS 

Contrasts results from the CONTRAST 

statements 

CONTRAST 

ConvergenceStatus convergence status default 

CorrB approximate correlation matrix of 

fixed-effects parameter estimates 

MODEL / CORRB 

CovB approximate covariance matrix of 

fixed-effects parameter estimates 

MODEL / COVB 

CovParms estimated covariance parameters default output 

Diffs differences of LS-means LSMEANS / DIFF (or PDIFF)




Dimensions dimensions of the model default output 

Estimates results from ESTIMATE statements ESTIMATE 

FitStatistics fit statistics default 

G estimated G matrix RANDOM / G 

GCorr correlation matrix from the 

estimated G matrix 

RANDOM / GCORR 

HLM1 Type 1 Hotelling-Lawley-McKeon MODEL / HTYPE=1 and 

tests of fixed effects 

REPEATED / HLM TYPE=UN 

HLM2 Type 2 Hotelling-Lawley-McKeon MODEL / HTYPE=2 and 



HLM3 Type 3 Hotelling-Lawley-McKeon 



HLPS1 Type 1 Hotelling-Lawley-Pillai- MODEL / HTYPE=1 and 

Samson tests of fixed effects REPEATED / HLPS TYPE=UN 

HLPS2 Type 2 Hotelling-Lawley-Pillai- MODEL / HTYPE=1 and 

Samson tests of fixed effects REPEATED / HLPS TYPE=UN 

HLPS3 Type 3 Hotelling-Lawley-Pillai- 

Samson tests of fixed effects 

REPEATED / HLPS TYPE=UN 

Influence influence diagnostics MODEL / INFLUENCE 

InfoCrit information criteria PROC MIXED IC 

InvCholG inverse Cholesky root of the 

estimated G matrix 

RANDOM / GCI 

InvCholR inverse Cholesky root of blocks of 

the estimated R matrix 

REPEATED / RCI 

InvCholV inverse Cholesky root of blocks of 

the estimated V matrix 

RANDOM / VCI 

InvCovB inverse of approximate covariance 

matrix of fixed-effects parameter estimates 

MODEL / COVBI 

InvG inverse of the estimated G 

matrix 

RANDOM / GI 

InvR inverse of blocks of the estimated R 

matrix 

REPEATED / RI 

InvV inverse of blocks of the estimated V 

matrix 

RANDOM / VI 

IterHistory iteration history default output 

LComponents single-degree-of-freedom estimates 

corresponding to rows of the L matrix 

for fixed effects 

MODEL / LCOMPONENTS 

LRT likelihood ratio test default output 

LSMeans LS-means LSMEANS 

MMEq mixed model equations PROC MIXED MMEQ 

MMEqSol mixed model equations solution PROC MIXED MMEQSOL 

ModelInfo model information default output




NObs number of observations read and 

used 


ParmSearch parameter search values PARMS 

Posterior posterior sampling information PRIOR 

R blocks of the estimated R matrix REPEATED / R 

RCorr correlation matrix from blocks of the 

estimated R matrix 

REPEATED / RCORR 

Search posterior density search table PRIOR / PSEARCH 

Slices tests of LS-means slices LSMEANS / SLICE= 

SolutionF fixed-effects solution vector MODEL / S 

SolutionR random-effects solution vector RANDOM / S 

Tests1 Type 1 tests of fixed effects MODEL / HTYPE=1 

Tests2 Type 2 tests of fixed effects MODEL / HTYPE=2 

Tests3 Type 3 tests of fixed effects default output 

Type1 Type 1 analysis of variance PROC MIXED METHOD=TYPE1 



Trans transformation of covariance parameters 

PRIOR / PTRANS 

V blocks of the estimated V matrix RANDOM / V 

VCorr correlation matrix from blocks of the 

estimated V matrix 

RANDOM / VCORR 

In Table 56.22, “Coef” refers to multiple tables produced by the E, E1, E2, or E3 option in the 

MODEL statement and the E option in the CONTRAST, ESTIMATE, and LSMEANS statements. 

You can create one large data set of these tables with a statement similar to the following: 

ods output Coef=c; 

To create separate data sets, use the following statement: 

ods output Coef(match_all)=c; 

Here the resulting data sets are named C, C1, C2, etc. The same principles apply to data sets created 

from the “R,” “CholR,” “InvCholR,” “RCorr,” “InvR,” “V,” “CholV,” “InvCholV,” “VCorr,” and 

“InvV” tables. 

In Table 56.22, the following changes have occurred from SAS 6. The “Predicted,” “PredMeans,” 

and “Sample” tables from SAS 6 no longer exist and have been replaced by output data sets; see 

descriptions of the MODEL statement options OUTPRED= and OUTPREDM= and the PRIOR 

statement option OUT= for more details. The “ML” and “REML” tables from SAS 6 have been 

replaced by the “IterHistory” table. The “Tests,” “HLM,” and “HLPS” tables from SAS 6 have 

been renamed “Tests3,” “HLM3,” and “HLPS3,” respectively. 

Table 56.23 lists the variable names associated with the data sets created when you use the ODS 

OUTPUT option in conjunction with the preceding tables. In Table 56.23, n is used to denote a


generic number that depends on the particular data set and model you select, and it can assume a 

different value each time it is used (even within the same table). The phrase model specific appears 

in rows of the affected tables to indicate that columns in these tables depend on the variables you 

specify in the model. 

CAUTION: There is a danger of name collisions with the variables in the model specific tables in 

Table 56.23 and variables in your input data set. You should avoid using input variables with the 

same names as the variables in these tables. 

Table 56.23 Variable Names for the ODS Tables Produced in PROC MIXED 

Table Name Variables 

AsyCorr Row, CovParm, CovP1–CovPn 

AsyCov Row, CovParm, CovP1–CovPn 

BaseDen Type, Parm1–Parmn 

Bound Technique, Converge, Iterations, Evaluations, LogBound, CovP1– 

CovPn, TCovP1–TCovPn 

CholG model specific, Effect, Subject, Sub1–Subn, Group, Group1– 

Groupn, Row, Col1–Coln 

CholR Index, Row, Col1–Coln 

CholV Index, Row, Col1–Coln 

ClassLevels Class, Levels, Values 

Coef model specific, LMatrix, Effect, Subject, Sub1–Subn, Group, 

Group1–Groupn, Row1–Rown 

Contrasts Label, NumDF, DenDF, ChiSquare, FValue, ProbChiSq, ProbF 

CorrB model specific, Effect, Row, Col1–Coln 

CovB model specific, Effect, Row, Col1–Coln 

CovParms CovParm, Subject, Group, Estimate, StandardError, ZValue, 

ProbZ, Alpha, Lower, Upper 

Diffs model specific, Effect, Margins, ByLevel, AT variables, Diff, StandardError, 

DF, tValue, Tails, Probt, Adjustment, Adjp, Alpha, 

Lower, Upper, AdjLow, AdjUpp 

Dimensions Descr, Value 

Estimates Label, Estimate, StandardError, DF, tValue, Tails, Probt, Alpha, 

Lower, Upper 

FitStatistics Descr, Value 

G model specific, Effect, Subject, Sub1–Subn, Group, Group1– 


GCorr model specific, Effect, Subject, Sub1–Subn, Group, Group1– 


HLM1 Effect, NumDF, DenDF, FValue, ProbF 



HLPS1 Effect, NumDF, DenDF, FValue, ProbF 

HLPS2 Effect, NumDF, DenDF, FValue, ProbF 

HLPS3 Effect, NumDF, DenDF, FValue, ProbF




Influence dependent on option modifiers, Effect, Tuple, Obs1–Obsk, Level, 

Iter, Index, Predicted, Residual, Leverage, PressRes, PRESS, Student, 

RMSE, RStudent, CookD, DFFITS, MDFFITS, CovRatio, 

CovTrace, CookDCP, MDFFITSCP, CovRatioCP, CovTraceCP, 

LD, RLD, Parm1–Parmp, CovP1–CovPq, Notes 

InfoCrit Neg2LogLike, Parms, AIC, AICC, HQIC, BIC, CAIC 

InvCholG model specific, Effect, Subject, Sub1–Subn, Group, Group1– 


InvCholR Index, Row, Col1–Coln 

InvCholV Index, Row, Col1–Coln 

InvCovB model specific, Effect, Row, Col1–Coln 

InvG model specific, Effect, Subject, Sub1–Subn, Group, Group1– 


InvR Index, Row, Col1–Coln 

InvV Index, Row, Col1–Coln 

IterHistory CovP1–CovPn, Iteration, Evaluations, M2ResLogLike, 

M2LogLike, Criterion 

LComponents Effect, TestType, LIndex, Estimate, StdErr, DF, tValue, Probt 

LRT DF, ChiSquare, ProbChiSq 

LSMeans model specific, Effect, Margins, ByLevel, AT variables, Estimate, 

StandardError, DF, tValue, Probt, Alpha, Lower, Upper, Cov1– 

Covn, Corr1–Corrn 

MMEq model specific, Effect, Subject, Sub1–Subn, Group, Group1– 


MMEqSol model specific, Effect, Subject, Sub1–Subn, Group, Group1– 


ModelInfo Descr, Value 

Nobs Label, N, NObsRead, NObsUsed, SumFreqsRead, SumFreqsUsed 

ParmSearch CovP1–CovPn, Var, ResLogLike, M2ResLogLike2, LogLike, 

M2LogLike, LogDetH 

Posterior Descr, Value 

R Index, Row, Col1–Coln 

RCorr Index, Row, Col1–Coln 

Search Parm, TCovP1–TCovPn, Posterior 

Slices model specific, Effect, Margins, ByLevel, AT variables, NumDF, 

DenDF, FValue, ProbF 

SolutionF model specific, Effect, Estimate, StandardError, DF, tValue, Probt, 

Alpha, Lower, Upper 

SolutionR model specific, Effect, Subject, Sub1–Subn, Group, Group1– 

Groupn, Estimate, StdErrPred, DF, tValue, Probt, Alpha, Lower, 

Upper 

Tests1 Effect, NumDF, DenDF, ChiSquare, FValue, ProbChiSq, ProbF 



Type1 Source, DF, SS, MS, EMS, ErrorTerm, ErrorDF, FValue, ProbF




Type2 Source, DF, SS, MS, EMS, ErrorTerm, ErrorDF, FValue, ProbF 

Type3 Source, DF, SS, MS, EMS, ErrorTerm, ErrorDF, FValue, ProbF 

Trans Prior, TCovP, CovP1–CovPn 

V Index, Row, Col1–Coln 

VCorr Index, Row, Col1–Coln 

Some of the variables listed in Table 56.23 are created only when you specify certain options in the 

relevant PROC MIXED statements. 

ODS Graphics 

This section describes the use of ODS for creating diagnostic plots with the MIXED procedure. 

To request these graphs you must specify the ODS GRAPHICS statement and the relevant options 

of the PROC MIXED or MODEL statement (Table 56.24). For more information about the ODS 

GRAPHICS statement, see Chapter 21, “Statistical Graphics Using ODS.” ODS names of the various 

graphics are given in the section “ODS Graph Names” on page 4002. 

Residual Plots 

The MIXED procedure can generate panels of residual diagnostics. Each panel consists of a plot 

of residuals versus predicted values, a histogram with normal density overlaid, a Q-Q plot, and 

summary residual and fit statistics (Figure 56.15). The plots are produced even if the OUTP= and 

OUTPM= options in the MODEL statement are not specified. Residual panels can be generated for 

marginal and conditional raw, studentized, and Pearson residuals as well as for scaled residuals (see 

the section “Residual Diagnostics” on page 3980). 

Recall the example in the section “Getting Started: MIXED Procedure” on page 3890. The following 

statements generate several 2 2 panels of residual graphs: 


proc mixed data=heights; 


model Height = Gender / residual; 

random Family Family*Gender; 

run; 

ods graphics off;

ODS Graphics ✦ 3999 

The graphical displays are requested by specifying the ODS GRAPHICS statement. The panel 

of the studentized marginal residuals is shown in Figure 56.15, and the panel of the studentized 

conditional residuals is shown in Figure 56.16. 

Figure 56.15 Panel of the Studentized (Marginal) Residuals 

Since the fixed-effects part of the model comprises only an intercept and the gender effect, the 

marginal mean takes on only two values, one for each gender. The “Residual Statistics” inset in the 

lower-right corner provides descriptive statistics for the set of residuals that is displayed. Note that 

residuals in a mixed model do not necessarily sum to zero, even if the model contains an intercept.


Figure 56.16 Panel of the Conditional Studentized Residuals 

Influence Plots 

The graphical features of the MIXED procedure enable you to generate plots of influence diagnostics 

and of deletion estimates. The type and number of plots produced depend on your modifiers of 

the INFLUENCE option in the MODEL statement and on the PLOTS= option in the PROC MIXED 

statement. Plots related to covariance parameters are produced only when diagnostics are computed 

by iterative methods (ITER=). The estimates of the fixed effects—and covariance parameters when 

updates are iterative—are plotted when you specify the ESTIMATES modifier or when you request 

PLOTS=INFLUENCEESTPLOT. 

Two basic types of influence panels are shown in Figure 56.17 and Figure 56.18. The diagnostics 

panel shows Cook’s D and CovRatio statistics for the fixed effects and the covariance parameters. 

For the SAS statements that produce these influence panels, see Example 56.8. In this example, the 

impact of subjects (Person) on the analysis is assessed. The Cook’s D statistic measures a subject’s 

impact on the estimates, and the CovRatio statistic measures a subject’s impact on the precision of 

the estimates. Separate statistics are computed for the fixed effects and the covariance parameters. 

The CovRatio statistic has a threshold of 1.0. Values larger than 1.0 indicate that precision of the 

estimates is lost by exclusion of the observations in question. Values smaller than 1.0 indicate that


precision is gained by exclusion of the observations from the analysis. For example, it is evident 

from Output 56.17 that person 20 has considerable impact on the covariance parameter estimates 

and moderate influence on the fixed-effects estimates. Furthermore, exclusion of this subject from 

the analysis increases the precision of the covariance parameters, whereas the effect on the precision 

of the fixed effects is minor. 

Output 56.18 shows another type of influence plot, a panel of the deletion estimates. Each plot 

within the panel corresponds to one of the model parameters. A reference line is drawn at the 

estimate based on the full data. 

Figure 56.17 Influence Diagnostics


Figure 56.18 Deletion Estimates 

ODS Graph Names 

To request graphics with PROC MIXED, you must first enable ODS Graphics by specifying the ODS 

GRAPHICS ON statement. See Chapter 21, “Statistical Graphics Using ODS,” for more information. 

Some graphs are produced by default; other graphs are produced by using statements and options. 

You can reference every graph produced through ODS Graphics with a name. The names of the 

graphs that PROC MIXED generates are listed in Table 56.24, along with the required statements 

and options. 

Table 56.24 ODS Graphics Produced by PROC MIXED 

ODS Graph Name Plot Description Statement or Option 

Boxplot Box plots PLOTS=BOXPLOT 

CovRatioPlot CovRatio statistics for fixed 

effects or covariance parame- 

ters 

CooksDPlot Cook’s D for fixed effects or 


PLOTS=INFLUENCESTATPANEL(UNPACK) 

and MODEL / INFLUENCE 

PLOTS=INFLUENCESTATPANEL(UNPACK) 

and MODEL / INFLUENCE


ODS Graph Name Plot Description Statement or Option 


DistancePlot Likelihood or restricted likelihood 

distance 

MODEL / INFLUENCE 

InfluenceEstPlot Panel of deletion estimates MODEL / INFLUENCE(EST) 

or PLOTS=INFLUENCEESTPLOT and 

MODEL / INFLUENCE 

InfluenceEstPlot Parameter estimates after removing 

observation or sets of 

observations 

PLOTS=INFLUENCEESTPLOT(UNPACK) 

and MODEL / INFLUENCE 

InfluenceStatPanel Panel of influence statistics MODEL / INFLUENCE 

PearsonBoxPlot Box plot of Pearson residuals PLOTS=PEARSONPANEL(UNPACK BOX) 

PearsonByPredicted Pearson residuals vs. 

dictedpre- 

PLOTS=PEARSONPANEL(UNPACK) 

PearsonHistogram Histogram of Pearson residuals 

PLOTS=PEARSONPANEL(UNPACK) 

PearsonPanel Panel of Pearson residuals MODEL / RESIDUAL 

PearsonQQplot Q-Q plot of Pearson residuals PLOTS=PEARSONPANEL(UNPACK) 

PressPlot Plot of PRESS residuals or 

PRESS statistic 

PLOTS=PRESS and MODEL / INFLUENCE 

ResidualBoxplot Box plot of (raw) residuals PLOTS=RESIDUALPANEL(UNPACK BOX) 

ResidualByPredicted Residuals vs. predicted PLOTS=RESIDUALPANEL(UNPACK) 

ResidualHistogram Histogram of raw residuals PLOTS=RESIDUALPANEL(UNPACK) 

ResidualPanel Panel of (raw) residuals MODEL / RESIDUAL 

ResidualQQplot Q-Q plot of raw residuals PLOTS=RESIDUALPANEL(UNPACK) 

ScaledBoxplot Box plot of scaled residuals PLOTS=VCIRYPANEL(UNPACK BOX) 

ScaledByPredicted Scaled residuals vs. predicted PLOTS=VCIRYPANEL(UNPACK) 

ScaledHistogram Histogram of scaled residuals PLOTS=VCIRYPANEL(UNPACK) 

ScaledQQplot Q-Q plot of scaled residuals PLOTS=VCIRYPANEL(UNPACK) 

StudentBoxplot Box plot of studentized residuals 

PLOTS=STUDENTPANEL(UNPACK BOX) 

StudentByPredicted Studentized residuals vs. predicted 

PLOTS=STUDENTPANEL(UNPACK) 

StudentHistogram Histogram 

residuals 

of studentized PLOTS=STUDENTPANEL(UNPACK) 

StudentPanel Panel of studentized residuals MODEL / RESIDUAL 

StudentQQplot Q-Q plot of studentized residuals 

PLOTS=STUDENTPANEL(UNPACK) 

VCIRYPanel Panel of scaled residuals MODEL / VCIRY


Computational Issues 

Computational Method 

In addition to numerous matrix-multiplication routines, PROC MIXED frequently uses the sweep 

operator (Goodnight 1979) and the Cholesky root (Golub and Van Loan 1989). The routines perform 

a modified W transformation (Goodnight and Hemmerle 1979) for G-side likelihood calculations 

and a direct method for R-side likelihood calculations. For the Type 3 F tests, PROC MIXED 

uses the algorithm described in Chapter 39, “The GLM Procedure.” 

PROC MIXED uses a ridge-stabilized Newton-Raphson algorithm to optimize either a full (ML) 

or residual (REML) likelihood function. The Newton-Raphson algorithm is preferred to the EM 

algorithm (Lindstrom and Bates 1988). PROC MIXED profiles the likelihood with respect to the 

fixed effects and also with respect to the residual variance whenever it appears reasonable to do 

so. The residual profiling can be avoided by using the NOPROFILE option of the PROC MIXED 

statement. PROC MIXED uses the MIVQUE0 method (Rao 1972; Giesbrecht 1989) to compute 

initial values. 

The likelihoods that PROC MIXED optimizes are usually well-defined continuous functions with a 

single optimum. The Newton-Raphson algorithm typically performs well and finds the optimum in 

a few iterations. It is a quadratically converging algorithm, meaning that the error of the approximation 

near the optimum is squared at each iteration. The quadratic convergence property is evident 

when the convergence criterion drops to zero by factors of 10 or more. 

Table 56.25 Notation for Order Calculations 

Symbol Number 

p columns of X 

g columns of Z 

N observations 

q covariance parameters 

t maximum observations per subject 

S subjects 

Using the notation from Table 56.25, the following are estimates of the computational speed of the 

algorithms used in PROC MIXED. For likelihood calculations, the crossproducts matrix construction 

is of order N.p C g/ 2 and the sweep operations are of order .p C g/ 3 . The first derivative 

calculations for parameters in G are of order qg 3 for ML and q.g 3 Cpg 2 Cp 2 g/ for REML. If you 

specify a subject effect in the RANDOM statement and if you are not using the REPEATED statement, 

then replace g with g=S and q with qS in these calculations. The first derivative calculations 

for parameters in R are of order qS.t 3 C gt 2 C g 2 t/ for ML and qS.t 3 C .p C g/t 2 C .p 2 C g 2 /t/ 

for REML. For the second derivatives, replace q with q.q C 1/=2 in the first derivative expressions. 

When you specify both G- and R-side parameters (that is, when you use both the RANDOM and 

REPEATED statements), then additional calculations are required of an order equal to the sum of 

the orders for G and R. Considerable execution times can result in this case.

Computational Issues ✦ 4005 

For further details about the computational techniques used in PROC MIXED, see Wolfinger, Tobias, 

and Sall (1994). 

Parameter Constraints 

By default, some covariance parameters are assumed to satisfy certain boundary constraints during 

the Newton-Raphson algorithm. For example, variance components are constrained to be nonnegative, 

and autoregressive parameters are constrained to be between 1 and 1. You can remove these 

constraints with the NOBOUND option in the PARMS statement (or with the NOBOUND option 

in the PROC MIXED statement), but this can lead to estimates that produce an infinite likelihood. 

You can also introduce or change boundary constraints with the LOWERB= and UPPERB= options 

in the PARMS statement. 

During the Newton-Raphson algorithm, a parameter might be set equal to one of its boundary 

constraints for a few iterations and then it might move away from the boundary. You see a missing 

value in the Criterion column of the “Iteration History” table whenever a boundary constraint is 

dropped. 

For some data sets the final estimate of a parameter might equal one of its boundary constraints. 

This is usually not a cause for concern, but it might lead you to consider a different model. For 

instance, a variance component estimate can equal zero; in this case, you might want to drop the 

corresponding random effect from the model. However, be aware that changing the model in this 

fashion can affect degrees-of-freedom calculations. 

Convergence Problems 

For some data sets, the Newton-Raphson algorithm can fail to converge. Nonconvergence can result 

from a number of causes, including flat or ridged likelihood surfaces and ill-conditioned data. 

It is also possible for PROC MIXED to converge to a point that is not the global optimum of the 

likelihood, although this usually occurs only with the spatial covariance structures. 

If you experience convergence problems, the following points might be helpful: 

One useful tool is the PARMS statement, which lets you input initial values for the covariance 

parameters and performs a grid search over the likelihood surface. 

Sometimes the Newton-Raphson algorithm does not perform well when two of the covariance 

parameters are on a different scale—that is, when they are several orders of magnitude apart. 

This is because the Hessian matrix is processed jointly for the two parameters, and elements 

of it corresponding to one of the parameters can become close to internal tolerances in PROC 

MIXED. In this case, you can improve stability by rescaling the effects in the model so that 

the covariance parameters are on the same scale. 

Data that are extremely large or extremely small can adversely affect results because of the 

internal tolerances in PROC MIXED. Rescaling it can improve stability. 

For stubborn problems, you might want to specify ODS OUTPUT COVPARMS=data-setname 

to output the “Covariance Parameter Estimates” table as a precautionary measure. That


way, if the problem does not converge, you can read the final parameter values back into a 

new run with the PARMSDATA= option in the PARMS statement. 

Fisher scoring can be more robust than Newton-Raphson with poor MIVQUE(0) starting 

values. Specifying a SCORING= value of 5 or so might help to recover from poor starting 

values. 

Tuning the singularity options SINGULAR=, SINGCHOL=, and SINGRES= in the MODEL 

statement can improve the stability of the optimization process. 

Tuning the MAXITER= and MAXFUNC= options in the PROC MIXED statement can save 

resources. Also, the ITDETAILS option displays the values of all the parameters at each 

iteration. 

Using the NOPROFILE and NOBOUND options in the PROC MIXED statement might help 

convergence, although they can produce unusual results. 

Although the CONVH convergence criterion usually gives the best results, you might want 

to try CONVF or CONVG, possibly along with the ABSOLUTE option. 

If the convergence criterion reaches a relatively small value such as 1E 7 but never gets 

lower than 1E 8, you might want to specify CONVH=1E 6 in the PROC MIXED statement 

to get results; however, interpret the results with caution. 

An infinite likelihood during the iteration process means that the Newton-Raphson algorithm 

has stepped into a region where either the R or V matrix is nonpositive definite. This is 

usually no cause for concern as long as iterations continue. If PROC MIXED stops because 

of an infinite likelihood, recheck your model to make sure that no observations from the same 

subject are producing identical rows in R or V and that you have enough data to estimate the 

particular covariance structure you have selected. Any time that the final estimated likelihood 

is infinite, subsequent results should be interpreted with caution. 

A nonpositive definite Hessian matrix can indicate a surface saddlepoint or linear dependencies 

among the parameters. 

A warning message about the singularities of X changing indicates that there is some linear 

dependency in the estimate of X 0bV 1 X that is not found in X 0 X. This can adversely affect 

the likelihood calculations and optimization process. If you encounter this problem, make 

sure that your model specification is reasonable and that you have enough data to estimate 

the particular covariance structure you have selected. Rearranging effects in the MODEL 

statement so that the most significant ones are first can help, because PROC MIXED sweeps 

the estimate of X 0 V 1 X in the order of the MODEL effects and the sweep is more stable 

if larger pivots are dealt with first. If this does not help, specifying starting values with the 

PARMS statement can place the optimization on a different and possibly more stable path. 

Lack of convergence can indicate model misspecification or a violation of the normality assumption.

Memory 

Computational Issues ✦ 4007 

Let p be the number of columns in X, and let g be the number of columns in Z. For large models, 

most of the memory resources are required for holding symmetric matrices of order p, g, and pCg. 

The approximate memory requirement in bytes is 

40.p 2 C g 2 / C 32.p C g/ 2 

If you have a large model that exceeds the memory capacity of your computer, see the suggestions 

listed under “Computing Time.” 

Computing Time 

PROC MIXED is computationally intensive, and execution times can be long. In addition to the 

CPU time used in collecting sums and crossproducts and in solving the mixed model equations (as 

in PROC GLM), considerable CPU time is often required to compute the likelihood function and 

its derivatives. These latter computations are performed for every Newton-Raphson iteration. 

If you have a model that takes too long to run, the following suggestions can be helpful: 

Examine the “Model Information” table to find out the number of columns in the X and Z 

matrices. A large number of columns in either matrix can greatly increase computing time. 

You might want to eliminate some higher-order effects if they are too large. 

If you have a Z matrix with a lot of columns, use the DDFM=BW option in the MODEL 

statement to eliminate the time required for the containment method. 

If possible, “factor out” a common effect from the effects in the RANDOM statement and 

make it the SUBJECT= effect. This creates a block-diagonal G matrix and can often speed 

calculations. 

If possible, use the same or nested SUBJECT= effects in all RANDOM and REPEATED 

statements. 

If your data set is very large, you might want to analyze it in pieces. The BY statement can 

help implement this strategy. 

In general, specify random effects with a lot of levels in the REPEATED statement and those 

with a few levels in the RANDOM statement. 

The METHOD=MIVQUE0 option runs faster than either the METHOD=REML or 

METHOD=ML option because it is noniterative. 

You can specify known values for the covariance parameters by using the HOLD= or 

NOITER option in the PARMS statement or the GDATA= option in the RANDOM statement. 

This eliminates the need for iteration. 

The LOGNOTE option in the PROC MIXED statement writes periodic messages to the SAS 

log concerning the status of the calculations. It can help you diagnose where the slowdown is 

occurring.


Examples: Mixed Procedure 

The following are basic examples of the use of PROC MIXED. More examples and details can be 

found in Littell et al. (2006), Wolfinger (1997), Verbeke and Molenberghs (1997, 2000), Murray 

(1998), Singer (1998), Sullivan, Dukes, and Losina (1999), and Brown and Prescott (1999). 

Example 56.1: Split-Plot Design 

PROC MIXED can fit a variety of mixed models. One of the most common mixed models is the 

split-plot design. The split-plot design involves two experimental factors, A and B. Levels of A are 

randomly assigned to whole plots (main plots), and levels of B are randomly assigned to split plots 

(subplots) within each whole plot. The design provides more precise information about B than about 

A, and it often arises when A can be applied only to large experimental units. An example is where 

A represents irrigation levels for large plots of land and B represents different crop varieties planted 

in each large plot. 

Consider the following data from Stroup (1989a), which arise from a balanced split-plot design with 

the whole plots arranged in a randomized complete-block design. The variable A is the whole-plot 

factor, and the variable B is the subplot factor. A traditional analysis of these data involves the 

construction of the whole-plot error (A*Block) to test A and the pooled residual error (B*Block and 

A*B*Block) to test B and A*B. To carry out this analysis with PROC GLM, you must use a TEST 

statement to obtain the correct F test for A. 

Performing a mixed model analysis with PROC MIXED eliminates the need for the error term 

construction. PROC MIXED estimates variance components for Block, A*Block, and the residual, 

and it automatically incorporates the correct error terms into test statistics. 

The following statements create a DATA set for a split-plot design with four blocks, three whole-plot 

levels, and two subplot levels: 

data sp; 

input Block A B Y @@; 

datalines; 

1 1 1 56 1 1 2 41 

1 2 1 50 1 2 2 36 

1 3 1 39 1 3 2 35 

2 1 1 30 2 1 2 25 

2 2 1 36 2 2 2 28 

2 3 1 33 2 3 2 30 

3 1 1 32 3 1 2 24 

3 2 1 31 3 2 2 27 

3 3 1 15 3 3 2 19 

4 1 1 30 4 1 2 25 

4 2 1 35 4 2 2 30 

4 3 1 17 4 3 2 18 

;

The following statements fit the split-plot model assuming random block effects: 

proc mixed; 

class A B Block; 



run; 

Example 56.1: Split-Plot Design ✦ 4009 

The variables A, B, and Block are listed as classification variables in the CLASS statement. The 

columns of model matrix X consist of indicator variables corresponding to the levels of the fixed 

effects A, B, and A*B listed on the right side of the MODEL statement. The dependent variable Y is 

listed on the left side of the MODEL statement. 

The columns of the model matrix Z consist of indicator variables corresponding to the levels of the 

random effects Block and A*Block. The G matrix is diagonal and contains the variance components 

of Block and A*Block. The R matrix is also diagonal and contains the residual variance. 

The SAS statements produce Output 56.1.1–Output 56.1.8. 

The “Model Information” table in Output 56.1.1 lists basic information about the split-plot model. 

REML is used to estimate the variance components, and the residual variance is profiled from the 

optimization. 

Output 56.1.1 Results for Split-Plot Analysis 



Data Set WORK.SP 

Dependent Variable Y 






The “Class Level Information” table in Output 56.1.2 lists the levels of all variables specified in the 

CLASS statement. You can check this table to make sure that the data are correct. 

Output 56.1.2 Split-Plot Example (continued) 



A 3 1 2 3 

B 2 1 2 

Block 4 1 2 3 4 

The “Dimensions” table in Output 56.1.3 lists the magnitudes of various vectors and matrices. The 

X matrix is seen to be 24 12, and the Z matrix is 24 16.



Dimensions 




Subjects 1 


The “Number of Observations” table in Output 56.1.4 shows that all observations read from the data 

set are used in the analysis. 






PROC MIXED estimates the variance components for Block, A*Block, and the residual by REML. 

The REML estimates are the values that maximize the likelihood of a set of linearly independent 

error contrasts, and they provide a correction for the downward bias found in the usual maximum 

likelihood estimates. The objective function is 2 times the logarithm of the restricted likelihood, 

and PROC MIXED minimizes this objective function to obtain the estimates. 

The minimization method is the Newton-Raphson algorithm, which uses the first and second derivatives 

of the objective function to iteratively find its minimum. The “Iteration History” table in 

Output 56.1.5 records the steps of that optimization process. For this example, only one iteration 

is required to obtain the estimates. The Evaluations column reveals that the restricted likelihood 

is evaluated once for each of the iterations. A criterion of 0 indicates that the Newton-Raphson 

algorithm has converged. 

Output 56.1.5 Split-Plot Analysis (continued) 



0 1 139.81461222 

1 1 119.76184570 0.00000000 


The REML estimates for the variance components of Block, A*Block, and the residual are 62.40, 

15.38, and 9.36, respectively, as listed in the Estimate column of the “Covariance Parameter Estimates” 

table in Output 56.1.6.



Estimates 


Block 62.3958 

A*Block 15.3819 


Example 56.1: Split-Plot Design ✦ 4011 

The “Fit Statistics” table in Output 56.1.7 lists several pieces of information about the fitted mixed 

model, including the residual log likelihood. The Akaike (AIC) and Bayesian (BIC) information 

criteria can be used to compare different models; the ones with smaller values are preferred. The 

AICC information criteria is a small-sample bias-adjusted form of the Akaike criterion (Hurvich 

and Tsai 1989). 







Finally, the fixed effects are tested by using Type 3 estimable functions (Output 56.1.8). 



Num Den 


A 2 6 4.07 0.0764 

B 1 9 19.39 0.0017 

A*B 2 9 4.02 0.0566 

The tests match the one obtained from the following PROC GLM statements: 

proc glm data=sp; 


model Y = A B A*B Block A*Block; 

test h=A e=A*Block; 

run; 

You can continue this analysis by producing solutions for the fixed and random effects and then 

testing various linear combinations of them by using the CONTRAST and ESTIMATE statements. 

If you use the same CONTRAST and ESTIMATE statements with PROC GLM, the test statistics


correspond to the fixed-effects-only model. The test statistics from PROC MIXED incorporate the 

random effects. 

The various “inference space” contrasts given by Stroup (1989a) can be implemented via the 

ESTIMATE statement. Consider the following examples: 

proc mixed data=sp; 




estimate ’a1 mean narrow’ 

intercept 1 A 1 B .5 .5 A*B .5 .5 | 

Block .25 .25 .25 .25 

A*Block .25 .25 .25 .25 0 0 0 0 0 0 0 0; 

estimate ’a1 mean intermed’ 

intercept 1 A 1 B .5 .5 A*B .5 .5 | 

Block .25 .25 .25 .25; 

estimate ’a1 mean broad’ 

intercept 1 a 1 b .5 .5 A*B .5 .5; 

run; 

These statements result in Output 56.1.9. 

Output 56.1.9 Inference Space Results 


Estimates 

Standard 

Label Estimate Error DF t Value Pr > |t| 

a1 mean narrow 32.8750 1.0817 9 30.39

proc mixed; 




run; 

An equivalent way of specifying this model is as follows: 




random intercept A / subject=Block; 

run; 

Example 56.2: Repeated Measures ✦ 4013 

In general, if all of the effects in the RANDOM statement can be nested within one effect, you 

can specify that one effect by using the SUBJECT= option. The subject effect is, in a sense, “factored 

out” of the random effects. The specification that uses the SUBJECT= effect can result in 

faster execution times for large problems because PROC MIXED is able to perform the likelihood 

calculations separately for each subject. 

Example 56.2: Repeated Measures 

The following data are from Pothoff and Roy (1964) and consist of growth measurements for 11 

girls and 16 boys at ages 8, 10, 12, and 14. Some of the observations are suspect (for example, the 

third observation for person 20); however, all of the data are used here for comparison purposes. 

The analysis strategy employs a linear growth curve model for the boys and girls as well as a 

variance-covariance model that incorporates correlations for all of the observations arising from 

the same person. The data are assumed to be Gaussian, and their likelihood is maximized to estimate 

the model parameters. See Jennrich and Schluchter (1986), Louis (1988), Crowder and Hand 

(1990), Diggle, Liang, and Zeger (1994), and Everitt (1995) for overviews of this approach to repeated 

measures. Jennrich and Schluchter present results for the Pothoff and Roy data from various 

covariance structures. The PROC MIXED statements to fit an unstructured variance matrix (their 

Model 2) are as follows: 

data pr; 

input Person Gender $ y1 y2 y3 y4; 

y=y1; Age=8; output; 




drop y1-y4; 

datalines; 

1 F 21.0 20.0 21.5 23.0 

2 F 21.0 21.5 24.0 25.5 

3 F 20.5 24.0 24.5 26.0 

4 F 23.5 24.5 25.0 26.5 

5 F 21.5 23.0 22.5 23.5


6 F 20.0 21.0 21.0 22.5 

7 F 21.5 22.5 23.0 25.0 

8 F 23.0 23.0 23.5 24.0 

9 F 20.0 21.0 22.0 21.5 

10 F 16.5 19.0 19.0 19.5 

11 F 24.5 25.0 28.0 28.0 

12 M 26.0 25.0 29.0 31.0 

13 M 21.5 22.5 23.0 26.5 

14 M 23.0 22.5 24.0 27.5 

15 M 25.5 27.5 26.5 27.0 

16 M 20.0 23.5 22.5 26.0 

17 M 24.5 25.5 27.0 28.5 

18 M 22.0 22.0 24.5 26.5 

19 M 24.0 21.5 24.5 25.5 

20 M 23.0 20.5 31.0 26.0 

21 M 27.5 28.0 31.0 31.5 

22 M 23.0 23.0 23.5 25.0 

23 M 21.5 23.5 24.0 28.0 

24 M 17.0 24.5 26.0 29.5 

25 M 22.5 25.5 25.5 26.0 

26 M 23.0 24.5 26.0 30.0 

27 M 22.0 21.5 23.5 25.0 

; 

proc mixed data=pr method=ml covtest; 

class Person Gender; 

model y = Gender Age Gender*Age / s; 

repeated / type=un subject=Person r; 

run; 

To follow Jennrich and Schluchter, this example uses maximum likelihood (METHOD=ML) instead 

of the default REML to estimate the unknown covariance parameters. The COVTEST option 

requests asymptotic tests of all the covariance parameters. 

The MODEL statement first lists the dependent variable Y. The fixed effects are then listed after the 

equal sign. The variable Gender requests a different intercept for the girls and boys, Age models 

an overall linear growth trend, and Gender*Age makes the slopes different over time. It is actually 

not necessary to specify Age separately, but doing so enables PROC MIXED to carry out a test for 

heterogeneous slopes. The S option requests the display of the fixed-effects solution vector. 

The REPEATED statement contains no effects, taking advantage of the default assumption that the 

observations are ordered similarly for each subject. The TYPE=UN option requests an unstructured 

block for each SUBJECT=Person. The R matrix is, therefore, block diagonal with 27 blocks, each 

block consisting of identical 4 4 unstructured matrices. The 10 parameters of these unstructured 

blocks make up the covariance parameters estimated by maximum likelihood. The R option requests 

that the first block of R be displayed. 

The results from this analysis are shown in Output 56.2.1–Output 56.2.9.


Output 56.2.1 Repeated Measures Analysis with Unstructured Covariance Matrix 



Data Set WORK.PR 

Dependent Variable y 

Covariance Structure Unstructured 

Subject Effect Person 

Estimation Method ML 

Residual Variance Method None 


Degrees of Freedom Method Between-Within 

In Output 56.2.1, the covariance structure is listed as “Unstructured,” and no residual variance is 

used with this structure. The default degrees-of-freedom method here is “Between-Within.” 

Output 56.2.2 Repeated Measures Analysis (continued) 



Person 27 1 2 3 4 5 6 7 8 9 10 11 12 13 

14 15 16 17 18 19 20 21 22 23 

24 25 26 27 

Gender 2 F M 

In Output 56.2.2, note that Person has 27 levels and Gender has 2. 


Dimensions 




Subjects 27 


In Output 56.2.3, the 10 covariance parameters result from the 4 4 unstructured blocks of R. There 

is no Z matrix for this model, and each of the 27 subjects has a maximum of 4 observations.








Iteration Evaluations -2 Log Like Criterion 

0 1 478.24175986 

1 2 419.47721707 0.00000152 

2 1 419.47704812 0.00000000 


Three Newton-Raphson iterations are required to find the maximum likelihood estimates 

(Output 56.2.4). The default relative Hessian criterion has a final value less than 1E 8, indicating 

the convergence of the Newton-Raphson algorithm and the attainment of an optimum. 


Estimated R Matrix for Person 1 

Row Col1 Col2 Col3 Col4 

1 5.1192 2.4409 3.6105 2.5222 

2 2.4409 3.9279 2.7175 3.0624 

3 3.6105 2.7175 5.9798 3.8235 

4 2.5222 3.0624 3.8235 4.6180 

The 4 4 matrix in Output 56.2.5 is the estimated unstructured covariance matrix. It is the estimate 

of the first block of R, and the other 26 blocks all have the same estimate.




Standard Z 

Cov Parm Subject Estimate Error Value Pr Z 

UN(1,1) Person 5.1192 1.4169 3.61 0.0002 

UN(2,1) Person 2.4409 0.9835 2.48 0.0131 

UN(2,2) Person 3.9279 1.0824 3.63 0.0001 

UN(3,1) Person 3.6105 1.2767 2.83 0.0047 

UN(3,2) Person 2.7175 1.0740 2.53 0.0114 

UN(3,3) Person 5.9798 1.6279 3.67 0.0001 

UN(4,1) Person 2.5222 1.0649 2.37 0.0179 

UN(4,2) Person 3.0624 1.0135 3.02 0.0025 

UN(4,3) Person 3.8235 1.2508 3.06 0.0022 

UN(4,4) Person 4.6180 1.2573 3.67 0.0001 

The “Covariance Parameter Estimates” table in Output 56.2.6 lists the 10 estimated covariance parameters 

in order; note their correspondence to the first block of R displayed in Output 56.2.5. The 

parameter estimates are labeled according to their location in the block in the Cov Parm column, 

and all of these estimates are associated with Person as the subject effect. The Std Error column lists 

approximate standard errors of the covariance parameters obtained from the inverse Hessian matrix. 

These standard errors lead to approximate Wald Z statistics, which are compared with the standard 

normal distribution The results of these tests indicate that all the parameters are significantly different 

from 0; however, the Wald test can be unreliable in small samples. 

To carry out Wald tests of various linear combinations of these parameters, use the following procedure. 

First, run the statements again, adding the ASYCOV option and an ODS statement: 

ods output CovParms=cp AsyCov=asy; 

proc mixed data=pr method=ml covtest asycov; 



repeated / type=un subject=Person r; 

run; 

This creates two data sets, cp and asy, which contain the covariance parameter estimates and their 

asymptotic variance covariance matrix, respectively. Then read these data sets into the SAS/IML 

matrix programming language as follows: 

proc iml; 

use cp; 

read all var {Estimate} into est; 

use asy; 

read all var (’CovP1’:’CovP10’) into asy; 

You can then construct your desired linear combinations and corresponding quadratic forms with 

the asy matrix.




-2 Log Likelihood 419.5 





DF Chi-Square Pr > ChiSq 

9 58.76 |t| 

Intercept 15.8423 0.9356 25 16.93



Num Den 


Gender 1 25 1.17 0.2904 

Age 1 25 110.54


This specifies an unstructured covariance matrix for the random intercept and slope. In mixed model 

notation, G is block diagonal with identical 2 2 unstructured blocks for each person. By default, R 

becomes 2 I. See Example 56.5 for further information about this model. 

Finally, you can fit a compound symmetry structure by using TYPE=CS, as follows: 

proc mixed data=pr method=ml covtest; 



repeated / type=cs subject=Person r; 

run; 

The results from this analysis are shown in Output 56.2.10–Output 56.2.17. 

The “Model Information” table in Output 56.2.10 is the same as before except for the change in 

“Covariance Structure.” 

Output 56.2.10 Repeated Measures Analysis with Compound Symmetry Structure 



Data Set WORK.PR 


Covariance Structure Compound Symmetry 

Subject Effect Person 

Estimation Method ML 




The “Dimensions” table in Output 56.2.11 shows that there are only two covariance parameters 

in the compound symmetry model; this covariance structure has common variance and common 

covariance. 

Output 56.2.11 Analysis with Compound Symmetry (continued) 



Person 27 1 2 3 4 5 6 7 8 9 10 11 12 13 

14 15 16 17 18 19 20 21 22 23 

24 25 26 27 

Gender 2 F M

Output 56.2.11 continued 

Dimensions 




Subjects 27 







Since the data are balanced, only one step is required to find the estimates (Output 56.2.12). 




0 1 478.24175986 

1 1 428.63905802 0.00000000 


Output 56.2.13 displays the estimated R matrix for the first subject. Note the compound symmetry 

structure here, which consists of a common covariance with a diagonal enhancement. 


Estimated R Matrix for Person 1 

Row Col1 Col2 Col3 Col4 

1 4.9052 3.0306 3.0306 3.0306 

2 3.0306 4.9052 3.0306 3.0306 

3 3.0306 3.0306 4.9052 3.0306 

4 3.0306 3.0306 3.0306 4.9052 

The common covariance is estimated to be 3:0306, as listed in the CS row of the “Covariance Parameter 

Estimates” table in Output 56.2.14, and the residual variance is estimated to be 1:8746, as 

listed in the Residual row. You can use these two numbers to estimate the intraclass correlation coefficient 

(ICC) for this model. Here, the ICC estimate equals 3:0306=.3:0306 C 1:8746/ D 0:6178. 

You can also obtain this number by adding the RCORR option to the REPEATED statement.




Standard Z 

Cov Parm Subject Estimate Error Value Pr Z 

CS Person 3.0306 0.9552 3.17 0.0015 

Residual 1.8746 0.2946 6.36 ChiSq 

1 49.60 |t| 

Intercept 16.3406 0.9631 25 16.97



Num Den 


Gender 1 25 0.47 0.5003 

Age 1 79 111.10


Output 56.2.19 Analysis with Heterogeneous Structures (continued) 



Person 27 1 2 3 4 5 6 7 8 9 10 11 12 13 

14 15 16 17 18 19 20 21 22 23 

24 25 26 27 

Gender 2 F M 

Dimensions 




Subjects 27 






As Output 56.2.20 shows, even with the heterogeneity, only one iteration is required for convergence. 




0 1 478.24175986 

1 1 408.81297228 0.00000000 


The “Covariance Parameter Estimates” table in Output 56.2.21 lists the heterogeneous estimates. 

Note that both the common covariance and the diagonal enhancement differ between girls and boys. 



Cov Parm Subject Group Estimate 

Variance Person Gender F 0.5900 

CS Person Gender F 3.8804 

Variance Person Gender M 2.7577 

CS Person Gender M 2.4463


As Output 56.2.22 shows, both Akaike’s information criterion (424.8) and Schwarz’s Bayesian 

information criterion (435.2) are smaller for this model than for the homogeneous compound symmetry 

model (440.6 and 448.4, respectively). This indicates that the heterogeneous model is more 

appropriate. To construct the likelihood ratio test between the two models, subtract the 2 log 

likelihood values: 428:6 408:8 D 19:8. Comparing this value with the 2 distribution with two 

degrees of freedom yields a p-value less than 0.0001, again favoring the heterogeneous model. 



-2 Log Likelihood 408.8 






3 69.43 |t| 

Intercept 16.3406 1.1130 25 14.68




Num Den 


Gender 1 25 0.55 0.4644 

Age 1 79 141.37


Example 56.3: Plotting the Likelihood ✦ 4027 

The “Model Information” table in Output 56.3.1 lists details about this variance components model. 

Output 56.3.1 Model Information 



Data Set WORK.HH 







The “Class Level Information” table in Output 56.3.2 lists the levels for A and B. 

Output 56.3.2 Class Level Information 



a 3 1 2 3 

b 2 1 2 

The “Dimensions” table in Output 56.3.3 reveals that X is 16 4 and Z is 16 8. Since there are 

no SUBJECT= effects, PROC MIXED considers the data to be effectively from one subject with 16 

observations. 

Output 56.3.3 Model Dimensions and Number of Observations 

Dimensions 




Subjects 1 






Only a portion of the “Parameter Search” table is shown in Output 56.3.4 because the full listing 

has 651 rows.


Output 56.3.4 Selected Results of Parameter Search 


-2 Res Log 

CovP1 CovP2 CovP3 Variance Res Log Like Like 

17.0000 0.3000 1.0000 80.1400 -52.4699 104.9399 

17.0000 0.3050 1.0000 80.0466 -52.4697 104.9393 

17.0000 0.3100 1.0000 79.9545 -52.4694 104.9388 

17.0000 0.3150 1.0000 79.8637 -52.4692 104.9384 

17.0000 0.3200 1.0000 79.7742 -52.4691 104.9381 

17.0000 0.3250 1.0000 79.6859 -52.4690 104.9379 

17.0000 0.3300 1.0000 79.5988 -52.4689 104.9378 

17.0000 0.3350 1.0000 79.5129 -52.4689 104.9377 

17.0000 0.3400 1.0000 79.4282 -52.4689 104.9377 

17.0000 0.3450 1.0000 79.3447 -52.4689 104.9378 

. . . . . . 

. . . . . . 

. . . . . . 

20.0000 0.3550 1.0000 78.2003 -52.4683 104.9366 

20.0000 0.3600 1.0000 78.1201 -52.4684 104.9368 

20.0000 0.3650 1.0000 78.0409 -52.4685 104.9370 

20.0000 0.3700 1.0000 77.9628 -52.4687 104.9373 

20.0000 0.3750 1.0000 77.8857 -52.4689 104.9377 

20.0000 0.3800 1.0000 77.8096 -52.4691 104.9382 

20.0000 0.3850 1.0000 77.7345 -52.4693 104.9387 

20.0000 0.3900 1.0000 77.6603 -52.4696 104.9392 

20.0000 0.3950 1.0000 77.5871 -52.4699 104.9399 

20.0000 0.4000 1.0000 77.5148 -52.4703 104.9406 

As Output 56.3.5 shows, convergence occurs quickly because PROC MIXED starts from the best 

value from the grid search. 

Output 56.3.5 Iteration History and Convergence Status 



1 2 104.93416367 0.00000000 


The “Covariance Parameter Estimates” table in Output 56.3.6 lists the variance components estimates. 

Note that B is much more variable than A*B.

Output 56.3.6 Estimated Covariance Parameters 



Standard Z 

Cov Parm Estimate Error Value Pr > Z 

b 1464.36 2098.01 0.70 0.2426 

a*b 26.9581 59.6570 0.45 0.3257 

Residual 78.8426 35.3512 2.23 0.0129 

The asymptotic covariance matrix in Output 56.3.7 also reflects the large variability of B relative to 

A*B. 

Output 56.3.7 Asymptotic Covariance Matrix of Covariance Parameters 

Asymptotic Covariance Matrix of Estimates 

Row Cov Parm CovP1 CovP2 CovP3 

1 b 4401640 1.2831 -273.32 

2 a*b 1.2831 3558.96 -502.84 

3 Residual -273.32 -502.84 1249.71 

As Output 56.3.8 shows, the PARMS likelihood ratio test (LRT) compares the best model from the 

grid search with the final fitted model. Since these models are nearly the same, the LRT is not 

significant. 

Output 56.3.8 Fit Statistics and Likelihood Ratio Test 






PARMS Model Likelihood Ratio Test 


2 0.00 1.0000 

The mixed model equations are analogous to the normal equations in the standard linear model. 

As Output 56.3.9 shows, for this example, rows 1–4 correspond to the fixed effects, rows 5–12 

correspond to the random effects, and Col13 corresponds to the dependent variable.


Output 56.3.9 Mixed Model Equations 

Mixed Model Equations 

Row Effect a b Col1 Col2 Col3 Col4 Col5 Col6 Col7 

1 Intercept 0.2029 0.06342 0.07610 0.06342 0.1015 0.1015 0.03805 

2 a 1 0.06342 0.06342 0.03805 0.02537 0.03805 

3 a 2 0.07610 0.07610 0.03805 0.03805 

4 a 3 0.06342 0.06342 0.02537 0.03805 

5 b 1 0.1015 0.03805 0.03805 0.02537 0.1022 0.03805 

6 b 2 0.1015 0.02537 0.03805 0.03805 0.1022 

7 a*b 1 1 0.03805 0.03805 0.03805 0.07515 

8 a*b 1 2 0.02537 0.02537 0.02537 

9 a*b 2 1 0.03805 0.03805 0.03805 

10 a*b 2 2 0.03805 0.03805 0.03805 

11 a*b 3 1 0.02537 0.02537 0.02537 

12 a*b 3 2 0.03805 0.03805 0.03805 


Row Col8 Col9 Col10 Col11 Col12 Col13 

1 0.02537 0.03805 0.03805 0.02537 0.03805 36.4143 

2 0.02537 13.8757 

3 0.03805 0.03805 12.7469 

4 0.02537 0.03805 9.7917 

5 0.03805 0.02537 21.2956 

6 0.02537 0.03805 0.03805 15.1187 

7 9.3477 

8 0.06246 4.5280 

9 0.07515 7.2676 

10 0.07515 5.4793 

11 0.06246 4.6802 

12 0.07515 5.1115 

The solution matrix in Output 56.3.10 results from sweeping all but the last row of the mixed model 

equations matrix. The final column contains a solution vector for the fixed and random effects. The 

first four rows correspond to fixed effects and the last eight correspond to random effects.

Output 56.3.10 Solutions of the Mixed Model Equations 

Mixed Model Equations Solution 


Row Effect a b Col1 Col2 Col3 Col4 Col5 Col6 Col7 

1 Intercept 761.84 -29.7718 -29.6578 -731.14 -733.22 -0.4680 

2 a 1 -29.7718 59.5436 29.7718 -2.0764 2.0764 -14.0239 

3 a 2 -29.6578 29.7718 56.2773 -1.0382 1.0382 0.4680 

4 a 3 

5 b 1 -731.14 -2.0764 -1.0382 741.63 722.73 -4.2598 

6 b 2 -733.22 2.0764 1.0382 722.73 741.63 4.2598 

7 a*b 1 1 -0.4680 -14.0239 0.4680 -4.2598 4.2598 22.8027 

8 a*b 1 2 0.4680 -12.9342 -0.4680 4.2598 -4.2598 4.1555 

9 a*b 2 1 -0.5257 1.0514 -12.9534 -4.7855 4.7855 2.1570 

10 a*b 2 2 0.5257 -1.0514 -14.0048 4.7855 -4.7855 -2.1570 

11 a*b 3 1 -12.4663 12.9342 12.4663 -4.2598 4.2598 1.9200 

12 a*b 3 2 -14.4918 14.0239 14.4918 4.2598 -4.2598 -1.9200 


Row Col8 Col9 Col10 Col11 Col12 Col13 

1 0.4680 -0.5257 0.5257 -12.4663 -14.4918 159.61 

2 -12.9342 1.0514 -1.0514 12.9342 14.0239 53.2049 

3 -0.4680 -12.9534 -14.0048 12.4663 14.4918 7.8856 

4 

5 4.2598 -4.7855 4.7855 -4.2598 4.2598 26.8837 

6 -4.2598 4.7855 -4.7855 4.2598 -4.2598 -26.8837 

7 4.1555 2.1570 -2.1570 1.9200 -1.9200 3.0198 

8 22.8027 -2.1570 2.1570 -1.9200 1.9200 -3.0198 

9 -2.1570 22.5560 4.4021 2.1570 -2.1570 -1.7134 

10 2.1570 4.4021 22.5560 -2.1570 2.1570 1.7134 

11 -1.9200 2.1570 -2.1570 22.8027 4.1555 -0.8115 

12 1.9200 -2.1570 2.1570 4.1555 22.8027 0.8115 

The A factor is significant at the 5% level (Output 56.3.11). 

Output 56.3.11 Tests of Fixed Effects 


Num Den 


a 2 2 28.00 0.0345 

Output 56.3.12 shows that the significance of A appears to be from the difference between its first 

level and its other two levels.


Output 56.3.12 Least Squares Means for A Effect 

Least Squares Means 

Standard 

Effect a Estimate Error DF t Value Pr > |t| 

a 1 212.82 27.6014 2 7.71 0.0164 

a 2 167.50 27.5463 2 6.08 0.0260 

a 3 159.61 27.6014 2 5.78 0.0286 

Output 56.3.13 lists the predicted values from the model. These values are the sum of the fixedeffects 

estimates and the empirical best linear unbiased predictors (EBLUPs) of the random effects. 

Output 56.3.13 Predicted Values 

StdErr 

Obs a b y Pred Pred DF Alpha Lower Upper Resid 

1 1 1 237 242.723 4.72563 10 0.05 232.193 253.252 -5.7228 

2 1 1 254 242.723 4.72563 10 0.05 232.193 253.252 11.2772 

3 1 1 246 242.723 4.72563 10 0.05 232.193 253.252 3.2772 

4 1 2 178 182.916 5.52589 10 0.05 170.603 195.228 -4.9159 

5 1 2 179 182.916 5.52589 10 0.05 170.603 195.228 -3.9159 

6 2 1 208 192.670 4.70076 10 0.05 182.196 203.144 15.3297 

7 2 1 178 192.670 4.70076 10 0.05 182.196 203.144 -14.6703 

8 2 1 187 192.670 4.70076 10 0.05 182.196 203.144 -5.6703 

9 2 2 146 142.330 4.70076 10 0.05 131.856 152.804 3.6703 

10 2 2 145 142.330 4.70076 10 0.05 131.856 152.804 2.6703 

11 2 2 141 142.330 4.70076 10 0.05 131.856 152.804 -1.3297 

12 3 1 186 185.687 5.52589 10 0.05 173.374 197.999 0.3134 

13 3 1 183 185.687 5.52589 10 0.05 173.374 197.999 -2.6866 

14 3 2 142 133.542 4.72563 10 0.05 123.013 144.072 8.4578 

15 3 2 125 133.542 4.72563 10 0.05 123.013 144.072 -8.5422 

16 3 2 136 133.542 4.72563 10 0.05 123.013 144.072 2.4578 

To plot the likelihood surface by using ODS Graphics, use the following statements: 

proc template; 

define statgraph surface; 

begingraph; 

layout overlay3d; 

surfaceplotparm x=CovP1 y=CovP2 z=ResLogLike; 

endlayout; 

endgraph; 

end; 

run; 

proc sgrender data=parms template=surface; 

run; 

The results from this plot are shown in Output 56.3.14. The peak of the surface is the REML 

estimates for the B and A*B variance components.

Output 56.3.14 Plot of Likelihood Surface 

Example 56.4: Known G and R 

Example 56.4: Known G and R ✦ 4033 

This animal breeding example from Henderson (1984, p. 48) considers multiple traits. The data 

are artificial and consist of measurements of two traits on three animals, but the second trait of the 

third animal is missing. Assuming an additive genetic model, you can use PROC MIXED to predict 

the breeding value of both traits on all three animals and also to predict the second trait of the third 

animal. The data are as follows: 

data h; 

input Trait Animal Y; 

datalines; 

1 1 6 

1 2 8 

1 3 7 

2 1 9 

2 2 5 

2 3 . 

;


Both G and R are known. 

2 

2 1 1 2 1 1 

3 

6 

1 

6 

G D 6 1 

6 2 

4 1 

2 

:5 

1 

2 

:5 

2 

1 

:5 

1 

1 

3 

1:5 

2 

:5 

1:5 

3 

:5 7 

2 7 

1:5 7 

:75 5 

1 :5 2 1:5 :75 3 

2 

6 

R D 6 

4 

4 0 0 1 0 0 

0 4 0 0 1 0 

0 0 4 0 0 1 

1 0 0 5 0 0 

0 1 0 0 5 0 

0 0 1 0 0 5 

3 

7 

5 

In order to read G into PROC MIXED by using the GDATA= option in the RANDOM statement, 

perform the following DATA step: 

data g; 

input Row Col1-Col6; 

datalines; 

1 2 1 1 2 1 1 

2 1 2 .5 1 2 .5 

3 1 .5 2 1 .5 2 

4 2 1 1 3 1.5 1.5 

5 1 2 .5 1.5 3 .75 

6 1 .5 2 1.5 .75 3 

; 

The preceding data are in the dense representation for a GDATA= data set. You can also construct 

a data set with the sparse representation by using Row, Col, and Value variables, although this would 

require 21 observations instead of 6 for this example. 

The PROC MIXED statements are as follows: 

proc mixed data=h mmeq mmeqsol; 

class Trait Animal; 

model Y = Trait / noint s outp=predicted; 

random Trait*Animal / type=un gdata=g g gi s; 

repeated / type=un sub=Animal r ri; 

parms (4) (1) (5) / noiter; 

run; 

proc print data=predicted; 

run; 

The MMEQ and MMEQSOL options request the mixed model equations and their solution. The 

variables Trait and Animal are classification variables, and Trait defines the entire X matrix for the 

fixed-effects portion of the model, since the intercept is omitted with the NOINT option. The fixedeffects 

solution vector and predicted values are also requested by using the S and OUTP= options, 

respectively.


The random effect Trait*Animal leads to a Z matrix with six columns, the first five corresponding to 

the identity matrix and the last consisting of 0s. An unstructured G matrix is specified by using the 

TYPE=UN option, and it is read into PROC MIXED from a SAS data set by using the GDATA=G 

specification. The G and GI options request the display of G and G 1 , respectively. The S option 

requests that the random-effects solution vector be displayed. 

Note that the preceding R matrix is block diagonal if the data are sorted by animals. The 

REPEATED statement exploits this fact by requesting R to have unstructured 2 2 blocks corresponding 

to animals, which are the subjects. The R and RI options request that the estimated 2 2 

blocks for the first animal and its inverse be displayed. The PARMS statement lists the parameters 

of this 2 2 matrix. Note that the parameters from G are not specified in the PARMS statement 

because they have already been assigned by using the GDATA= option in the RANDOM statement. 

The NOITER option prevents PROC MIXED from computing residual (restricted) maximum likelihood 

estimates; instead, the known values are used for inferences. 


The “Unstructured” covariance structure (Output 56.4.1) applies to both G and R here. The levels 

of Trait and Animal have been specified correctly. 

Output 56.4.1 Model and Class Level Information 



Data Set WORK.H 



Subject Effect Animal 







Trait 2 1 2 

Animal 3 1 2 3 

The three covariance parameters indicated in Output 56.4.2 correspond to those from the R matrix. 

Those from G are considered fixed and known because of the GDATA= option.



Dimensions 




Subjects 1 






Because starting values for the covariance parameters are specified in the PARMS statement, the 

MIXED procedure prints the residual (restricted) log likelihood at the starting values. Because of 

the NOITER option in the PARMS statement, this is also the final log likelihood in this analysis 

(Output 56.4.3). 

Output 56.4.3 REML Log Likelihood 

Parameter Search 

CovP1 CovP2 CovP3 Res Log Like -2 Res Log Like 

4.0000 1.0000 5.0000 -7.3731 14.7463 

The block of R corresponding to the first animal and the inverse of this block are shown in 

Output 56.4.4. 

Output 56.4.4 Inverse R Matrix 

Estimated R Matrix 

for Animal 1 

Row Col1 Col2 

1 4.0000 1.0000 

2 1.0000 5.0000 

Estimated Inv(R) Matrix 

for Animal 1 

Row Col1 Col2 

1 0.2632 -0.05263 

2 -0.05263 0.2105


The G matrix as specified in the GDATA= data set and its inverse are shown in Output 56.4.5 and 

Output 56.4.6. 

Output 56.4.5 G Matrix 

Estimated G Matrix 

Row Effect Trait Animal Col1 Col2 Col3 Col4 

1 Trait*Animal 1 1 2.0000 1.0000 1.0000 2.0000 

2 Trait*Animal 1 2 1.0000 2.0000 0.5000 1.0000 

3 Trait*Animal 1 3 1.0000 0.5000 2.0000 1.0000 

4 Trait*Animal 2 1 2.0000 1.0000 1.0000 3.0000 

5 Trait*Animal 2 2 1.0000 2.0000 0.5000 1.5000 

6 Trait*Animal 2 3 1.0000 0.5000 2.0000 1.5000 

Output 56.4.6 Inverse G Matrix 

Estimated G Matrix 

Row Col5 Col6 

1 1.0000 1.0000 

2 2.0000 0.5000 

3 0.5000 2.0000 

4 1.5000 1.5000 

5 3.0000 0.7500 

6 0.7500 3.0000 

Estimated Inv(G) Matrix 


1 Trait*Animal 1 1 2.5000 -1.0000 -1.0000 -1.6667 

2 Trait*Animal 1 2 -1.0000 2.0000 0.6667 

3 Trait*Animal 1 3 -1.0000 2.0000 0.6667 

4 Trait*Animal 2 1 -1.6667 0.6667 0.6667 1.6667 

5 Trait*Animal 2 2 0.6667 -1.3333 -0.6667 

6 Trait*Animal 2 3 0.6667 -1.3333 -0.6667 

Estimated Inv(G) Matrix 

Row Col5 Col6 

1 0.6667 0.6667 

2 -1.3333 

3 -1.3333 

4 -0.6667 -0.6667 

5 1.3333 

6 1.3333 

The table of covariance parameter estimates in Output 56.4.7 displays only the parameters in R. 

Because of the GDATA= option in the RANDOM statement, the G-side parameters do not partici-


pate in the parameter estimation process. Because of the NOITER option in the PARMS statement, 

however, the R-side parameters in this output are identical to their starting values. 

Output 56.4.7 R-Side Covariance Parameters 


Cov Parm Subject Estimate 

UN(1,1) Animal 4.0000 

UN(2,1) Animal 1.0000 

UN(2,2) Animal 5.0000 

The coefficients of the mixed model equations in Output 56.4.8 agree with Henderson (1984, p. 55). 

Recall from Output 56.4.1 that there are 2 columns in X and 6 columns in Z. The first 8 columns 

of the mixed model equations correspond to the X and Z components. Column 9 represents the Y 

border. 

Output 56.4.8 Mixed Model Equations with Y Border 



1 Trait 1 0.7763 -0.1053 0.2632 0.2632 

2 Trait 2 -0.1053 0.4211 -0.05263 -0.05263 

3 Trait*Animal 1 1 0.2632 -0.05263 2.7632 -1.0000 

4 Trait*Animal 1 2 0.2632 -0.05263 -1.0000 2.2632 

5 Trait*Animal 1 3 0.2500 -1.0000 

6 Trait*Animal 2 1 -0.05263 0.2105 -1.7193 0.6667 

7 Trait*Animal 2 2 -0.05263 0.2105 0.6667 -1.3860 

8 Trait*Animal 2 3 0.6667 


Row Col5 Col6 Col7 Col8 Col9 

1 0.2500 -0.05263 -0.05263 4.6974 

2 0.2105 0.2105 2.2105 

3 -1.0000 -1.7193 0.6667 0.6667 1.1053 

4 0.6667 -1.3860 1.8421 

5 2.2500 0.6667 -1.3333 1.7500 

6 0.6667 1.8772 -0.6667 -0.6667 1.5789 

7 -0.6667 1.5439 0.6316 

8 -1.3333 -0.6667 1.3333 

The solution to the mixed model equations also matches that given by Henderson (1984, p. 55). 

After solving the augmented mixed model equations, you can find the solutions for fixed and random 

effects in the last column (Output 56.4.9).

Output 56.4.9 Solutions of the Mixed Model Equations with Y Border 




1 Trait 1 2.5508 1.5685 -1.3047 -1.1775 

2 Trait 2 1.5685 4.5539 -1.4112 -1.3534 

3 Trait*Animal 1 1 -1.3047 -1.4112 1.8282 1.0652 

4 Trait*Animal 1 2 -1.1775 -1.3534 1.0652 1.7589 

5 Trait*Animal 1 3 -1.1701 -0.9410 1.0206 0.7085 

6 Trait*Animal 2 1 -1.3002 -2.1592 1.8010 1.0900 

7 Trait*Animal 2 2 -1.1821 -2.1055 1.0925 1.7341 

8 Trait*Animal 2 3 -1.1678 -1.3149 1.0070 0.7209 



1 -1.1701 -1.3002 -1.1821 -1.1678 6.9909 

2 -0.9410 -2.1592 -2.1055 -1.3149 6.9959 

3 1.0206 1.8010 1.0925 1.0070 0.05450 

4 0.7085 1.0900 1.7341 0.7209 -0.04955 

5 1.7812 1.0095 0.7197 1.7756 0.02230 

6 1.0095 2.7518 1.6392 1.4849 0.2651 

7 0.7197 1.6392 2.6874 0.9930 -0.2601 

8 1.7756 1.4849 0.9930 2.7645 0.1276 

The solutions for the fixed and random effects in Output 56.4.10 correspond to the last column in 

Output 56.4.9. Note that the standard errors for the fixed effects and the prediction standard errors 

for the random effects are the square root values of the diagonal entries in the solution of the mixed 

model equations (Output 56.4.9). 

Output 56.4.10 Solutions for Fixed and Random Effects 

Solution for Fixed Effects 

Standard 

Effect Trait Estimate Error DF t Value Pr > |t| 

Trait 1 6.9909 1.5971 3 4.38 0.0221 

Trait 2 6.9959 2.1340 3 3.28 0.0465 

Solution for Random Effects 

Std Err 

Effect Trait Animal Estimate Pred DF t Value Pr > |t| 

Trait*Animal 1 1 0.05450 1.3521 0 0.04 . 

Trait*Animal 1 2 -0.04955 1.3262 0 -0.04 . 

Trait*Animal 1 3 0.02230 1.3346 0 0.02 . 

Trait*Animal 2 1 0.2651 1.6589 0 0.16 . 

Trait*Animal 2 2 -0.2601 1.6393 0 -0.16 . 

Trait*Animal 2 3 0.1276 1.6627 0 0.08 .


The estimates for the two traits are nearly identical, but the standard error of the second trait is 

larger because of the missing observation. 

The Estimate column in the “Solution for Random Effects” table lists the best linear unbiased predictions 

(BLUPs) of the breeding values of both traits for all three animals. The p-values are missing 

because the default containment method for computing degrees of freedom results in zero degrees 

of freedom for the random effects parameter tests. 

Output 56.4.11 Significance Test Comparing Traits 


Num Den 


Trait 2 3 10.59 0.0437 

The two estimated traits are significantly different from zero at the 5% level (Output 56.4.11). 

Output 56.4.12 displays the predicted values of the observations based on the trait and breeding 

value estimates—that is, the fixed and random effects. 

Output 56.4.12 Predicted Observations 

StdErr 

Obs Trait Animal Y Pred Pred DF Alpha Lower Upper Resid 

1 1 1 6 7.04542 1.33027 0 0.05 . . -1.04542 

2 1 2 8 6.94137 1.39806 0 0.05 . . 1.05863 

3 1 3 7 7.01321 1.41129 0 0.05 . . -0.01321 

4 2 1 9 7.26094 1.72839 0 0.05 . . 1.73906 

5 2 2 5 6.73576 1.74077 0 0.05 . . -1.73576 

6 2 3 . 7.12015 2.99088 0 0.05 . . . 

The predicted values are not the predictions of future records in the sense that they do not contain 

a component corresponding to a new observational error. See Henderson (1984) for information 

about predicting future records. The Lower and Upper columns usually contain confidence limits 

for the predicted values; they are missing here because the random-effects parameter degrees of 

freedom equals 0.

Example 56.5: Random Coefficients 

Example 56.5: Random Coefficients ✦ 4041 

This example comes from a pharmaceutical stability data simulation performed by Obenchain 

(1990). The observed responses are replicate assay results, expressed in percent of label claim, 

at various shelf ages, expressed in months. The desired mixed model involves three batches of 

product that differ randomly in intercept (initial potency) and slope (degradation rate). This type 

of model is also known as a hierarchical or multilevel model (Singer 1998; Sullivan, Dukes, and 

Losina 1999). 

The SAS statements are as follows: 

data rc; 

input Batch Month @@; 

Monthc = Month; 

do i = 1 to 6; 

input Y @@; 

output; 

end; 

datalines; 

1 0 101.2 103.3 103.3 102.1 104.4 102.4 

1 1 98.8 99.4 99.7 99.5 . . 

1 3 98.4 99.0 97.3 99.8 . . 

1 6 101.5 100.2 101.7 102.7 . . 

1 9 96.3 97.2 97.2 96.3 . . 

1 12 97.3 97.9 96.8 97.7 97.7 96.7 

2 0 102.6 102.7 102.4 102.1 102.9 102.6 

2 1 99.1 99.0 99.9 100.6 . . 

2 3 105.7 103.3 103.4 104.0 . . 

2 6 101.3 101.5 100.9 101.4 . . 

2 9 94.1 96.5 97.2 95.6 . . 

2 12 93.1 92.8 95.4 92.2 92.2 93.0 

3 0 105.1 103.9 106.1 104.1 103.7 104.6 

3 1 102.2 102.0 100.8 99.8 . . 

3 3 101.2 101.8 100.8 102.6 . . 

3 6 101.1 102.0 100.1 100.2 . . 

3 9 100.9 99.5 102.2 100.8 . . 

3 12 97.8 98.3 96.9 98.4 96.9 96.5 

; 


class Batch; 

model Y = Month / s; 

random Int Month / type=un sub=Batch s; 

run; 

In the DATA step, Monthc is created as a duplicate of Month in order to enable both a continuous and 

a classification version of the same variable. The variable Monthc is used in a subsequent analysis 

In the PROC MIXED statements, Batch is listed as the only classification variable. The fixed effect 

Month in the MODEL statement is not declared as a classification variable; thus it models a linear 

trend in time. An intercept is included as a fixed effect by default, and the S option requests that the


fixed-effects parameter estimates be produced. 

The two random effects are Int and Month, modeling random intercepts and slopes, respectively. 

Note that Intercept and Month are used as both fixed and random effects. The TYPE=UN option in 

the RANDOM statement specifies an unstructured covariance matrix for the random intercept and 

slope effects. In mixed model notation, G is block diagonal with unstructured 2 2 blocks. Each 

block corresponds to a different level of Batch, which is the SUBJECT= effect. The unstructured 

type provides a mechanism for estimating the correlation between the random coefficients. The S 

option requests the production of the random-effects parameter estimates. 

The results from this analysis are shown in Output 56.5.1–Output 56.5.9. The “Unstructured” covariance 

structure in Output 56.5.1 applies to G here. 

Output 56.5.1 Model Information in Random Coefficients Analysis 



Data Set WORK.RC 



Subject Effect Batch 





Batch is the only classification variable in this analysis, and it has three levels (Output 56.5.2). 

Output 56.5.2 Random Coefficients Analysis (continued) 



Batch 3 1 2 3 

The “Dimensions” table in Output 56.5.3 indicates that there are three subjects (corresponding to 

batches). The 24 observations not used correspond to the missing values of Y in the input data set. 


Dimensions 



Columns in Z Per Subject 2 

Subjects 3 

Max Obs Per Subject 36






As Output 56.5.4 shows, only one iteration is required for convergence. 





0 1 367.02768461 

1 1 350.32813577 0.00000000 


The Estimate column in Output 56.5.5 lists the estimated elements of the unstructured 2 2 matrix 

comprising the blocks of G. Note that the random coefficients are negatively correlated. 




UN(1,1) Batch 0.9768 

UN(2,1) Batch -0.1045 

UN(2,2) Batch 0.03717 


The null model likelihood ratio test indicates a significant improvement over the null model consisting 

of no random effects and a homogeneous residual error (Output 56.5.6). 






BIC (smaller is better) 354.7





3 16.70 0.0008 

The fixed-effects estimates represent the estimated means for the random intercept and slope, respectively 

(Output 56.5.7). 



Standard 

Effect Estimate Error DF t Value Pr > |t| 

Intercept 102.70 0.6456 2 159.08 |t| 

Intercept 1 -1.0010 0.6842 78 -1.46 0.1474 

Month 1 0.1287 0.1245 78 1.03 0.3047 

Intercept 2 0.3934 0.6842 78 0.58 0.5669 

Month 2 -0.2060 0.1245 78 -1.65 0.1021 

Intercept 3 0.6076 0.6842 78 0.89 0.3772 

Month 3 0.07731 0.1245 78 0.62 0.5365 

The F statistic in the “Type 3 Tests of Fixed Effects” table in Output 56.5.9 is the square of the 

t statistic used in the test of Month in the preceding “Solution for Fixed Effects” table (compare 

Output 56.5.7 and Output 56.5.9). Both statistics test the null hypothesis that the slope assigned to 

Month equals 0, and this hypothesis can barely be rejected at the 5% level.



Num Den 


Month 1 2 19.41 0.0478 


It is also possible to fit a random coefficients model with error terms that follow a nested structure 

(Fuller and Battese 1973). The following SAS statements represent one way of doing this: 


class Batch Monthc; 

model Y = Month / s; 

random Int Month Monthc / sub=Batch s; 

run; 

The variable Monthc is added to the CLASS and RANDOM statements, and it models the nested 

errors. Note that Month and Monthc are continuous and classification versions of the same variable. 

Also, the TYPE=UN option is dropped from the RANDOM statement, resulting in the default 

variance components model instead of correlated random coefficients. The results from this analysis 

are shown in Output 56.5.10. 

Output 56.5.10 Random Coefficients with Nested Errors Analysis 



Data Set WORK.RC 



Subject Effect Batch 







Batch 3 1 2 3 

Monthc 6 0 1 3 6 9 12 

Dimensions 



Columns in Z Per Subject 8 

Subjects 3 

Max Obs Per Subject 36









0 1 367.02768461 

1 4 277.51945360 . 

2 1 276.97551718 0.00104208 

3 1 276.90304909 0.00003174 

4 1 276.90100316 0.00000004 

5 1 276.90100092 0.00000000 




Intercept Batch 0 

Month Batch 0.01243 

Monthc Batch 3.7411 


For this analysis, the Newton-Raphson algorithm requires five iterations and nine likelihood evaluations 

to achieve convergence. The missing value in the Criterion column in iteration 1 indicates 

that a boundary constraint has been dropped. 

The estimate for the Intercept variance component equals 0. This occurs frequently in practice and 

indicates that the restricted likelihood is maximized by setting this variance component equal to 0. 

Whenever a zero variance component estimate occurs, the following note appears in the SAS log: 

NOTE: Estimated G matrix is not positive definite. 

The remaining variance component estimates are positive, and the estimate corresponding to the 

nested errors (MONTHC) is much larger than the other two. 

A comparison of AIC and BIC for this model with those of the previous model favors the nested 

error model (compare Output 56.5.11 and Output 56.5.6). Strictly speaking, a likelihood ratio test 

cannot be carried out between the two models because one is not contained in the other; however, a 

cautious comparison of likelihoods can be informative.


Output 56.5.11 Random Coefficients with Nested Errors Analysis (continued) 






The better-fitting covariance model affects the standard errors of the fixed-effects parameter estimates 

more than the estimates themselves (Output 56.5.12). 



Standard 


Intercept 102.56 0.7287 2 140.74



Solution for Random Effects 

Std Err 

Effect Batch Monthc Estimate Pred DF t Value Pr > |t| 

Intercept 1 0 . . . . 

Month 1 -0.00028 0.09268 66 -0.00 0.9976 

Monthc 1 0 0.2191 0.7896 66 0.28 0.7823 

Monthc 1 1 -2.5690 0.7571 66 -3.39 0.0012 

Monthc 1 3 -2.3067 0.6865 66 -3.36 0.0013 

Monthc 1 6 1.8726 0.7328 66 2.56 0.0129 

Monthc 1 9 -1.2350 0.9300 66 -1.33 0.1888 

Monthc 1 12 0.7736 1.1992 66 0.65 0.5211 

Intercept 2 0 . . . . 

Month 2 -0.07571 0.09268 66 -0.82 0.4169 

Monthc 2 0 -0.00621 0.7896 66 -0.01 0.9938 

Monthc 2 1 -2.2126 0.7571 66 -2.92 0.0048 

Monthc 2 3 3.1063 0.6865 66 4.53 F 

Month 1 2 15.78 0.0579 

The test of Month is similar to that from the previous model, although it is no longer significant at 

the 5% level (Output 56.5.14).

Example 56.6: Line-Source Sprinkler Irrigation 

Example 56.6: Line-Source Sprinkler Irrigation ✦ 4049 

These data appear in Hanks et al. (1980), Johnson, Chaudhuri, and Kanemasu (1983), and Stroup 

(1989b). Three cultivars (Cult) of winter wheat are randomly assigned to rectangular plots within 

each of three blocks (Block). The nine plots are located side by side, and a line-source sprinkler is 

placed through the middle. Each plot is subdivided into twelve subplots—six to the north of the 

line source, six to the south (Dir). The two plots closest to the line source represent the maximum 

irrigation level (Irrig=6), the two next-closest plots represent the next-highest level (Irrig=5), and so 

forth. 

This example is a case where both G and R can be modeled. One of Stroup’s models specifies a 

diagonal G containing the variance components for Block, Block*Dir, and Block*Irrig, and a Toeplitz 

R with four bands. The SAS statements to fit this model and carry out some further analyses follow. 

CAUTION: This analysis can require considerable CPU time. 

data line; 

length Cult$ 8; 

input Block Cult$ @; 

row = _n_; 

do Sbplt=1 to 12; 

if Sbplt le 6 then do; 

Irrig = Sbplt; 

Dir = ’North’; 

end; else do; 

Irrig = 13 - Sbplt; 

Dir = ’South’; 

end; 

input Y @; output; 

end; 

datalines; 

1 Luke 2.4 2.7 5.6 7.5 7.9 7.1 6.1 7.3 7.4 6.7 3.8 1.8 

1 Nugaines 2.2 2.2 4.3 6.3 7.9 7.1 6.2 5.3 5.3 5.2 5.4 2.9 

1 Bridger 2.9 3.2 5.1 6.9 6.1 7.5 5.6 6.5 6.6 5.3 4.1 3.1 

2 Nugaines 2.4 2.2 4.0 5.8 6.1 6.2 7.0 6.4 6.7 6.4 3.7 2.2 

2 Bridger 2.6 3.1 5.7 6.4 7.7 6.8 6.3 6.2 6.6 6.5 4.2 2.7 

2 Luke 2.2 2.7 4.3 6.9 6.8 8.0 6.5 7.3 5.9 6.6 3.0 2.0 

3 Nugaines 1.8 1.9 3.7 4.9 5.4 5.1 5.7 5.0 5.6 5.1 4.2 2.2 

3 Luke 2.1 2.3 3.7 5.8 6.3 6.3 6.5 5.7 5.8 4.5 2.7 2.3 

3 Bridger 2.7 2.8 4.0 5.0 5.2 5.2 5.9 6.1 6.0 4.3 3.1 3.1 

; 

proc mixed; 

class Block Cult Dir Irrig; 

model Y = Cult|Dir|Irrig@2; 

random Block Block*Dir Block*Irrig; 

repeated / type=toep(4) sub=Block*Cult r; 

lsmeans Cult|Irrig; 

estimate ’Bridger vs Luke’ Cult 1 -1 0; 

estimate ’Linear Irrig’ Irrig -5 -3 -1 1 3 5; 

estimate ’B vs L x Linear Irrig’ Cult*Irrig 

-5 -3 -1 1 3 5 5 3 1 -1 -3 -5; 

run;


The preceding statements use the bar operator ( | ) and the at sign (@) to specify all two-factor 

interactions between Cult, Dir, and Irrig as fixed effects. 

The RANDOM statement sets up the Z and G matrices corresponding to the random effects Block, 

Block*Dir, and Block*Irrig. 

In the REPEATED statement, the TYPE=TOEP(4) option sets up the blocks of the R matrix to be 

Toeplitz with four bands below and including the main diagonal. The subject effect is Block*Cult, 

and it produces nine 12 12 blocks. The R option requests that the first block of R be displayed. 

Least squares means (LSMEANS) are requested for Cult, Irrig, and Cult*Irrig, and a few ESTIMATE 

statements are specified to illustrate some linear combinations of the fixed effects. 

The results from this analysis are shown in Output 56.6.1. 

The “Covariance Structures” row in Output 56.6.1 reveals the two different structures assumed for 

G and R. 

Output 56.6.1 Model Information in Line-Source Sprinkler Analysis 



Data Set WORK.LINE 


Covariance Structures Variance Components, 

Toeplitz 

Subject Effect Block*Cult 





The levels of each classification variable are listed as a single string in the Values column, regardless 

of whether the levels are numeric or character (Output 56.6.2). 

Output 56.6.2 Class Level Information 



Block 3 1 2 3 

Cult 3 Bridger Luke Nugaines 

Dir 2 North South 

Irrig 6 1 2 3 4 5 6 

Even though there is a SUBJECT= effect in the REPEATED statement, the analysis considers all 

of the data to be from one subject because there is no corresponding SUBJECT= effect in the 

RANDOM statement (Output 56.6.3).



Dimensions 




Subjects 1 






The Newton-Raphson algorithm converges successfully in seven iterations (Output 56.6.4). 

Output 56.6.4 Iteration History and Convergence Status 



0 1 226.25427252 

1 4 187.99336173 . 

2 3 186.62579299 0.10431081 

3 1 184.38218213 0.04807260 

4 1 183.41836853 0.00886548 

5 1 183.25111475 0.00075353 

6 1 183.23809997 0.00000748 

7 1 183.23797748 0.00000000 


The first block of the estimated R matrix has the TOEP(4) structure, and the observations that are 

three plots apart exhibit a negative correlation (Output 56.6.5).


Output 56.6.5 Estimated R Matrix for the First Subject 

Estimated R Matrix for Block*Cult 1 Bridger 

Row Col1 Col2 Col3 Col4 Col5 Col6 Col7 

1 0.2850 0.007986 0.001452 -0.09253 

2 0.007986 0.2850 0.007986 0.001452 -0.09253 

3 0.001452 0.007986 0.2850 0.007986 0.001452 -0.09253 

4 -0.09253 0.001452 0.007986 0.2850 0.007986 0.001452 -0.09253 

5 -0.09253 0.001452 0.007986 0.2850 0.007986 0.001452 

6 -0.09253 0.001452 0.007986 0.2850 0.007986 

7 -0.09253 0.001452 0.007986 0.2850 

8 -0.09253 0.001452 0.007986 

9 -0.09253 0.001452 

10 -0.09253 

11 

12 

Estimated R Matrix for Block*Cult 1 Bridger 


1 

2 

3 

4 

5 -0.09253 

6 0.001452 -0.09253 

7 0.007986 0.001452 -0.09253 

8 0.2850 0.007986 0.001452 -0.09253 

9 0.007986 0.2850 0.007986 0.001452 -0.09253 

10 0.001452 0.007986 0.2850 0.007986 0.001452 

11 -0.09253 0.001452 0.007986 0.2850 0.007986 

12 -0.09253 0.001452 0.007986 0.2850 

Output 56.6.6 lists the estimated covariance parameters from both G and R. The first three are the 

variance components making up the diagonal G, and the final four make up the Toeplitz structure 

in the blocks of R. The Residual row corresponds to the variance of the Toeplitz structure, and it 

represents the parameter profiled out during the optimization process. 

Output 56.6.6 Estimated Covariance Parameters 



Block 0.2194 

Block*Dir 0.01768 

Block*Irrig 0.03539 

TOEP(2) Block*Cult 0.007986 

TOEP(3) Block*Cult 0.001452 

TOEP(4) Block*Cult -0.09253 

Residual 0.2850


The “ 2 Res Log Likelihood” value in Output 56.6.7 is the same as the final value listed in the 

“Iteration History” table (Output 56.6.4). 

Output 56.6.7 Fit Statistics Based on the Residual Log Likelihood 






Every fixed effect except for Dir and Cult*Irrig is significant at the 5% level (Output 56.6.8). 

Output 56.6.8 Tests for Fixed Effects 


Num Den 


Cult 2 68 7.98 0.0008 

Dir 1 2 3.95 0.1852 

Cult*Dir 2 68 3.44 0.0379 

Irrig 5 10 102.60


Output 56.6.10 Least Squares Means for Cult, Irrig, and Their Interaction 

Least Squares Means 

Standard 

Effect Cult Irrig Estimate Error DF t Value Pr > |t| 

Cult Bridger 5.0306 0.2874 68 17.51

Example 56.7: Influence in Heterogeneous Variance Model ✦ 4055 

Example 56.7: Influence in Heterogeneous Variance Model 

In this example from Snedecor and Cochran (1976, p. 256), a one-way classification model with 

heterogeneous variances is fit. The data, shown in the following DATA step, represent amounts of 

different types of fat absorbed by batches of doughnuts during cooking, measured in grams. 

data absorb; 

input FatType Absorbed @@; 

datalines; 

1 164 1 172 1 168 1 177 1 156 1 195 

2 178 2 191 2 197 2 182 2 185 2 177 

3 175 3 193 3 178 3 171 3 163 3 176 

4 155 4 166 4 149 4 164 4 170 4 168 

; 

The statistical model for these data can be written as 

Yij D C i C ij 

i D 1; ; t D 4 

j D 1; ; r D 6 

ij D N.0; 2 i / 

where Yij is the amount of fat absorbed by the j th batch of the ith fat type, and i denotes the 

fat-type effects. A quick glance at the data suggests that observations 6, 9, 14, and 21 might be 

influential on the analysis, because these are extreme observations for the respective fat types. 

The following SAS statements fit this model and request influence diagnostics for the fixed effects 

and covariance parameters. The ODS GRAPHICS statement requests plots of the influence diagnostics 

in addition to the tabular output. The ESTIMATES suboption requests plots of “leave-one-out” 

estimates for the fixed effects and group variances. 


proc mixed data=absorb asycov; 

class FatType; 

model Absorbed = FatType / s 

influence(iter=10 estimates); 

repeated / group=FatType; 

ods output Influence=inf; 

run; 

ods graphics off; 

The “Influence” table is output to the SAS data set inf so that parameter estimates can be printed 

subsequently. Results from this analysis are shown in Output 56.7.1.


Output 56.7.1 Heterogeneous Variance Analysis 



Data Set WORK.ABSORB 

Dependent Variable Absorbed 


Group Effect FatType 






Cov Parm Group Estimate 

Residual FatType 1 178.00 





Fat Standard 

Effect Type Estimate Error DF t Value Pr > |t| 

Intercept 162.00 3.3566 20 48.26


Output 56.7.2 Asymptotic Variances of Group Variance Estimates 

Asymptotic Covariance Matrix of Estimates 

Row Cov Parm CovP1 CovP2 CovP3 CovP4 

1 Residual 12674 

2 Residual 1459.26 

3 Residual 3810.30 

4 Residual 1827.90 

In groups where the residual variance estimate is large, the precision of the estimate is also small 

(Output 56.7.2). 

The following statements print the “leave-one-out” estimates for fixed effects and covariance parameters 

that were written to the inf data set with the ESTIMATES suboption (Output 56.7.3): 

proc print data=inf label; 

var parm1-parm5 covp1-covp4; 

run; 

Output 56.7.3 Leave-One-Out Estimates 

Residual Residual Residual Residual 

Fat Fat Fat Fat FatType FatType FatType FatType 

Obs Intercept Type 1 Type 2 Type 3 Type 4 1 2 3 4 

1 162.00 11.600 23.000 14.000 0 203.30 60.400 97.60 67.600 

2 162.00 10.000 23.000 14.000 0 222.47 60.400 97.60 67.600 

3 162.00 10.800 23.000 14.000 0 217.68 60.400 97.60 67.600 

4 162.00 9.000 23.000 14.000 0 214.99 60.400 97.60 67.600 

5 162.00 13.200 23.000 14.000 0 145.70 60.400 97.60 67.600 

6 162.00 5.400 23.000 14.000 0 63.80 60.400 97.60 67.600 

7 162.00 10.000 24.400 14.000 0 178.00 60.795 97.60 67.600 

8 162.00 10.000 21.800 14.000 0 178.00 64.691 97.60 67.600 

9 162.00 10.000 20.600 14.000 0 178.00 32.296 97.60 67.600 

10 162.00 10.000 23.600 14.000 0 178.00 72.797 97.60 67.600 

11 162.00 10.000 23.000 14.000 0 178.00 75.490 97.60 67.600 

12 162.00 10.000 24.600 14.000 0 178.00 56.285 97.60 67.600 

13 162.00 10.000 23.000 14.200 0 178.00 60.400 121.68 67.600 

14 162.00 10.000 23.000 10.600 0 178.00 60.400 35.30 67.600 

15 162.00 10.000 23.000 13.600 0 178.00 60.400 120.79 67.600 

16 162.00 10.000 23.000 15.000 0 178.00 60.400 114.50 67.600 

17 162.00 10.000 23.000 16.600 0 178.00 60.400 71.30 67.600 

18 162.00 10.000 23.000 14.000 0 178.00 60.400 121.98 67.600 

19 163.40 8.600 21.600 12.600 0 178.00 60.400 97.60 69.799 

20 161.20 10.800 23.800 14.800 0 178.00 60.400 97.60 79.698 

21 164.60 7.400 20.400 11.400 0 178.00 60.400 97.60 33.800 

22 161.60 10.400 23.400 14.400 0 178.00 60.400 97.60 83.292 

23 160.40 11.600 24.600 15.600 0 178.00 60.400 97.60 65.299 

24 160.80 11.200 24.200 15.200 0 178.00 60.400 97.60 73.677


The graphical displays in Output 56.7.4 and Output 56.7.5 are requested by specifying the ODS 

GRAPHICS statement. For general information about ODS Graphics, see Chapter 21, “Statistical 

Graphics Using ODS.” For specific information about the graphics available in the MIXED procedure, 

see the section “ODS Graphics” on page 3998. 

Output 56.7.4 Fixed-Effects Deletion Estimates

Output 56.7.5 Covariance Parameter Deletion Estimates 


The estimate of the intercept is affected only when observations from the last group are removed. 

The estimate of the “FatType 1” effect reacts to removal of observations in the first and last group 

(Output 56.7.4). 

While observations can affect one or more fixed-effects solutions in this model, they can affect only 

one covariance parameter, the variance in their group (Output 56.7.5). Observations 6, 9, 14, and 

21, which are extreme in their group, reduce the group variance considerably. 

Diagnostics related to residuals and predicted values are printed with the following statements: 


var observed predicted residual pressres 

student Rstudent; 

run;


Output 56.7.6 Residual Diagnostics 

Internally Externally 

Observed Predicted PRESS Studentized Studentized 

Obs Value Mean Residual Residual Residual Residual 

1 164 172.0 -8.000 -9.600 -0.6569 -0.6146 

2 172 172.0 0.000 0.000 0.0000 0.0000 

3 168 172.0 -4.000 -4.800 -0.3284 -0.2970 

4 177 172.0 5.000 6.000 0.4105 0.3736 

5 156 172.0 -16.000 -19.200 -1.3137 -1.4521 

6 195 172.0 23.000 27.600 1.8885 3.1544 

7 178 185.0 -7.000 -8.400 -0.9867 -0.9835 

8 191 185.0 6.000 7.200 0.8457 0.8172 

9 197 185.0 12.000 14.400 1.6914 2.3131 

10 182 185.0 -3.000 -3.600 -0.4229 -0.3852 

11 185 185.0 0.000 -0.000 0.0000 0.0000 

12 177 185.0 -8.000 -9.600 -1.1276 -1.1681 

13 175 176.0 -1.000 -1.200 -0.1109 -0.0993 

14 193 176.0 17.000 20.400 1.8850 3.1344 

15 178 176.0 2.000 2.400 0.2218 0.1993 

16 171 176.0 -5.000 -6.000 -0.5544 -0.5119 

17 163 176.0 -13.000 -15.600 -1.4415 -1.6865 

18 176 176.0 0.000 0.000 0.0000 0.0000 

19 155 162.0 -7.000 -8.400 -0.9326 -0.9178 

20 166 162.0 4.000 4.800 0.5329 0.4908 

21 149 162.0 -13.000 -15.600 -1.7321 -2.4495 

22 164 162.0 2.000 2.400 0.2665 0.2401 

23 170 162.0 8.000 9.600 1.0659 1.0845 

24 168 162.0 6.000 7.200 0.7994 0.7657 

Observations 6, 9, 14, and 21 have large studentized residuals (Output 56.7.6). That the externally 

studentized residuals are much larger than the internally studentized residuals for these observations 

indicates that the variance estimate in the group shrinks when the observation is removed. Also 

important to note is that comparisons based on raw residuals in models with heterogeneous variance 

can be misleading. Observation 5, for example, has a larger residual but a smaller studentized 

residual than observation 21. The variance for the first fat type is much larger than the variance in 

the fourth group. A “large” residual is more “surprising” in the groups with small variance. 

A measure of the overall influence on the analysis is the (restricted) likelihood distance, shown in 

Output 56.7.7. Observations 6, 9, 14, and 21 clearly displace the REML solution more than any 

other observations.

Output 56.7.7 Restricted Likelihood Distance 


The following statements list the restricted likelihood distance and various diagnostics related to the 

fixed-effects estimates (Output 56.7.8): 


var leverage observed CookD DFFITS CovRatio RLD; 

run;


Output 56.7.8 Restricted Likelihood Distance and Fixed-Effects Diagnostics 

Restr. 

Observed Cook’s Likelihood 

Obs Leverage Value D DFFITS COVRATIO Distance 

1 0.167 164 0.02157 -0.27487 1.3706 0.1178 

2 0.167 172 0.00000 -0.00000 1.4998 0.1156 

3 0.167 168 0.00539 -0.13282 1.4675 0.1124 

4 0.167 177 0.00843 0.16706 1.4494 0.1117 

5 0.167 156 0.08629 -0.64938 0.9822 0.5290 

6 0.167 195 0.17831 1.41069 0.4301 5.8101 

7 0.167 178 0.04868 -0.43982 1.2078 0.1935 

8 0.167 191 0.03576 0.36546 1.2853 0.1451 

9 0.167 197 0.14305 1.03446 0.6416 2.2909 

10 0.167 182 0.00894 -0.17225 1.4463 0.1116 

11 0.167 185 0.00000 -0.00000 1.4998 0.1156 

12 0.167 177 0.06358 -0.52239 1.1183 0.2856 

13 0.167 175 0.00061 -0.04441 1.4961 0.1151 

14 0.167 193 0.17766 1.40175 0.4340 5.7044 

15 0.167 178 0.00246 0.08915 1.4851 0.1139 

16 0.167 171 0.01537 -0.22892 1.4078 0.1129 

17 0.167 163 0.10389 -0.75423 0.8766 0.8433 

18 0.167 176 0.00000 0.00000 1.4998 0.1156 

19 0.167 155 0.04349 -0.41047 1.2390 0.1710 

20 0.167 166 0.01420 0.21950 1.4148 0.1124 

21 0.167 149 0.15000 -1.09545 0.6000 2.7343 

22 0.167 164 0.00355 0.10736 1.4786 0.1133 

23 0.167 170 0.05680 0.48500 1.1592 0.2383 

24 0.167 168 0.03195 0.34245 1.3079 0.1353 

In this example, observations with large likelihood distances also have large values for Cook’s D 

and values of CovRatio far less than one (Output 56.7.8). The latter indicates that the fixed effects 

are estimated more precisely when these observations are removed from the analysis. 

The following statements print the values of the D statistic and the CovRatio for the covariance 

parameters: 


var iter CookDCP CovRatioCP; 

run; 

The same conclusions as for the fixed-effects estimates hold for the covariance parameter estimates. 

Observations 6, 9, 14, and 21 change the estimates and their precision considerably (Output 56.7.9, 

Output 56.7.10). All iterative updates converged within at most four iterations.

Output 56.7.9 Covariance Parameter Diagnostics 


Cook’s D COVRATIO 

Obs Iterations CovParms CovParms 

1 3 0.05050 1.6306 

2 3 0.15603 1.9520 

3 3 0.12426 1.8692 

4 3 0.10796 1.8233 

5 4 0.08232 0.8375 

6 4 1.02909 0.1606 

7 1 0.00011 1.2662 

8 2 0.01262 1.4335 

9 3 0.54126 0.3573 

10 3 0.10531 1.8156 

11 3 0.15603 1.9520 

12 2 0.01160 1.0849 

13 3 0.15223 1.9425 

14 4 1.01865 0.1635 

15 3 0.14111 1.9141 

16 3 0.07494 1.7203 

17 3 0.18154 0.6671 

18 3 0.15603 1.9520 

19 2 0.00265 1.3326 

20 3 0.08008 1.7374 

21 1 0.62500 0.3125 

22 3 0.13472 1.8974 

23 2 0.00290 1.1663 

24 2 0.02020 1.4839 

Output 56.7.10 displays the standard panel of influence diagnostics that is obtained when influence 

analysis is iterative. The Cook’s D and CovRatio statistics are displayed for each deletion set for 

both fixed-effects and covariance parameter estimates. This provides a convenient summary of 

the impact on the analysis for each deletion set, since Cook’s D statistic measures impact on the 

estimates and the CovRatio statistic measures impact on the precision of the estimates.


Output 56.7.10 Influence Diagnostics 

Observations 6, 9, 14, and 21 have considerable impact on estimates and precision of fixed effects 

and covariance parameters. This is not necessarily the case. Observations can be influential on only 

some aspects of the analysis, as shown in the next example. 

Example 56.8: Influence Analysis for Repeated Measures Data 

This example revisits the repeated measures data of Pothoff and Roy (1964) that were analyzed 

in Example 56.2. Recall that the data consist of growth measurements at ages 8, 10, 12, and 14 

for 11 girls and 16 boys. The model being fit contains fixed effects for Gender and Age and their 

interaction. 

The earlier analysis of these data indicated some unusual observations in this data set. Because 

of the clustered data structure, it is of interest to study the influence of clusters (children) on the 

analysis rather than the influence of individual observations. A cluster comprises the repeated measurements 

for each child.

Example 56.8: Influence Analysis for Repeated Measures Data ✦ 4065 

The repeated measures are first modeled with an unstructured within-child variance-covariance matrix. 

A residual variance is not profiled in this model. A noniterative influence analysis will update 

the fixed effects only. The following statements request this noniterative maximum likelihood analysis 

and produce Output 56.8.1: 

proc mixed data=pr method=ml; 

class person gender; 

model y = gender age gender*age / 

influence(effect=person); 

repeated / type=un subject=person; 

ods select influence; 

run; 

Output 56.8.1 Default Influence Statistics in Noniterative Analysis 


Influence Diagnostics for Levels of Person 

Number of 

Observations PRESS Cook’s 

Person in Level Statistic D 

1 4 10.1716 0.01539 

2 4 3.8187 0.03988 

3 4 10.8448 0.02891 

4 4 24.0339 0.04515 

5 4 1.6900 0.01613 

6 4 11.8592 0.01634 

7 4 1.1887 0.00521 

8 4 4.6717 0.02742 

9 4 13.4244 0.03949 

10 4 85.1195 0.13848 

11 4 67.9397 0.09728 

12 4 40.6467 0.04438 

13 4 13.0304 0.00924 

14 4 6.1712 0.00411 

15 4 24.5702 0.12727 

16 4 20.5266 0.01026 

17 4 9.9917 0.01526 

18 4 7.9355 0.01070 

19 4 15.5955 0.01982 

20 4 42.6845 0.01973 

21 4 95.3282 0.10075 

22 4 13.9649 0.03778 

23 4 4.9656 0.01245 

24 4 37.2494 0.15094 

25 4 4.3756 0.03375 

26 4 8.1448 0.03470 

27 4 20.2913 0.02523 

Each observation in the “Influence Diagnostics for Levels of Person” table in Output 56.8.1 represents 

the removal of four observations. The subjects 10, 15, and 24 have the greatest impact on the 

fixed effects (Cook’s D), and subject 10 and 21 have large PRESS statistics. The 21st child has a


large PRESS statistic, and its D statistic is not that extreme. This is an indication that the model fits 

rather poorly for this child, whether it is part of the data or not. 

The previous analysis does not take into account the effect on the covariance parameters when a 

subject is removed from the analysis. If you also update the covariance parameters, the impact of 

observations on these can amplify or allay their effect on the fixed effects. To assess the overall 

influence of subjects on the analysis and to compute separate statistics for the fixed effects and covariance 

parameters, an iterative analysis is obtained by adding the INFLUENCE suboption ITER=, 

as follows: 


proc mixed data=pr method=ml; 



influence(effect=person iter=5); 

repeated / type=un subject=person; 

run; 

The number of additional iterations following removal of the observations for a particular subject 

is limited to five. Graphical displays of influence diagnostics are requested by specifying the ODS 

GRAPHICS statement. For general information about ODS Graphics, see Chapter 21, “Statistical 

Graphics Using ODS.” For specific information about the graphics available in the MIXED procedure, 

see the section “ODS Graphics” on page 3998. 

The MIXED procedure produces a plot of the restricted likelihood distance (Output 56.8.2) and a 

panel of diagnostics for fixed effects and covariance parameters (Output 56.8.3).

Output 56.8.2 Restricted Likelihood Distance 

Example 56.8: Influence Analysis for Repeated Measures Data ✦ 4067


Output 56.8.3 Influence Diagnostics Panel 

As judged by the restricted likelihood distance, subjects 20 and 24 clearly have the most influence 

on the overall analysis (Output 56.8.2). 

Output 56.8.3 displays Cook’s D and CovRatio statistics for the fixed effects and covariance parameters. 

Clearly, subject 20 has a dramatic effect on the estimates of variances and covariances. 

This subject also affects the precision of the covariance parameter estimates more than any other 

subject in Output 56.8.3 (CovRatio near 0). 

The child who exerts the greatest influence on the fixed effects is subject 24. Maybe surprisingly, 

this subject affects the variance-covariance matrix of the fixed effects more than subject 20 (small 

CovRatio in Output 56.8.3). 

The final model investigated for these data is a random coefficient model as in Stram and Lee (1994) 

with random effects for the intercept and age effect. The following statements examine the estimates 

for fixed effects and the entries of the unstructured 2 2 variance matrix of the random coefficients 

graphically:


proc mixed data=pr method=ml 

plots(only)=InfluenceEstPlot; 



influence(iter=5 effect=person est); 


run; 

The PLOTS(ONLY)=INFLUENCEESTPLOT option restricts the graphical output from this PROC 

MIXED run to only the panels of deletion estimates (Output 56.8.4 and Output 56.8.5). 

Output 56.8.4 Fixed-Effects Deletion Estimates 

In Output 56.8.4 the graphs on the left side of the panel represent the intercept and slope estimate 

for boys; the graphs on the right side represent the difference in intercept and slope between boys 

and girls. Removing any one of the first eleven children, who are girls, does not alter the intercept 

or slope in the group of boys. The difference in these parameters between boys and girls is altered 

by the removal of any child. Subject 24 changes the fixed effects considerably, subject 20 much less 

so.


Output 56.8.5 Covariance Parameter Deletion Estimates 

The covariance parameter deletion estimates in Output 56.8.5 show several important features. 

The panels do not contain information about subject 24. Estimation of the G matrix following 

removal of that child did not yield a positive definite matrix. As a consequence, covariance 

parameter diagnostics are not produced for this subject. 

Subject 20 has great impact on the four covariance parameters. Removing this child from 

the analysis increases the variance of the random intercept and random slope and reduces 

the residual variance by almost 80%. The repeated measurements of this child exhibit an 

up-and-down behavior. 

The variance of the random intercept and slope are reduced when child 15 is removed from 

the analysis. This child’s growth measurements oscillate about 27.0 from age 10 on. 

Examining observed and residual values by levels of classification variables is also a useful tool to 

diagnose the adequacy of the model and unusual observations. Box plots for effects in the model that 

consist of only classification variables can be requested with the BOXPLOT option of the PLOT= 

option in the PROC MIXED statement. For example, the following statements produce box plots 

for the SUBJECT= effects in the model:


proc mixed data=pr method=ml 

plot=boxplot(observed marginal conditional subject); 


model y = gender age gender*age; 


run; 

The specific boxplot options request a plot of the observed data (Output 56.8.6), the marginal residuals 

(Output 56.8.7), and the conditional residuals (Output 56.8.8). Box plots of the observed values 

show the variation within and between children clearly. The group of girls (subjects 1–11) is distinguishable 

from the group of boys by somewhat lesser average growth and lesser within-child 

variation (Output 56.8.6). After adjusting for overall (population-averaged) gender and age effects, 

the residual within-child variation is reduced but substantial differences in the means remain 

(Output 56.8.7). If child-specific inferences are desired, a model accounting for only Gender, Age, 

and Gender*Age effects is not adequate for these data. 

Output 56.8.6 Distribution of Observed Values


Output 56.8.7 Distribution of Marginal Residuals 

The conditional residuals incorporate the EBLUPs for each child and enable you to examine whether 

the subject-specific model is adequate (Output 56.8.8). By using each child “as its own control,” 

the residuals are now centered near zero. Subjects 20 and 24 stand out as unusual in all three sets of 

box plots.

Output 56.8.8 Distribution of Conditional Residuals 

Example 56.9: Examining Individual Test Components ✦ 4073 

Example 56.9: Examining Individual Test Components 

The LCOMPONENTS option in the MODEL statement enables you to perform single-degree-offreedom 

tests for individual rows of the L matrix. Such tests are useful to identify interaction 

patterns. In a balanced layout, Type 3 components of L associated with A*B interactions correspond 

to simple contrasts of cell mean differences. 

The first example revisits the data from the split-plot design by Stroup (1989a) that was analyzed 

in Example 56.1. Recall that variables A and B in the following statements represent the whole-plot 

and subplot factors, respectively: 


class a b block; 

model y = a b a*b / LComponents e3; 


run; 

The MIXED procedure constructs a separate L matrix for each of the three fixed-effects components. 

The matrices are displayed in Output 56.9.1. The tests for fixed effects are shown in


Output 56.9.2. 

Output 56.9.1 Coefficients of Type 3 Estimable Functions 


Type 3 Coefficients for A 

Effect A B Row1 Row2 

Intercept 

A 1 1 

A 2 1 

A 3 -1 -1 

B 1 

B 2 

A*B 1 1 0.5 

A*B 1 2 0.5 

A*B 2 1 0.5 

A*B 2 2 0.5 

A*B 3 1 -0.5 -0.5 

A*B 3 2 -0.5 -0.5 

Type 3 Coefficients for B 

Effect A B Row1 

Intercept 

A 1 

A 2 

A 3 

B 1 1 

B 2 -1 

A*B 1 1 0.3333 

A*B 1 2 -0.333 

A*B 2 1 0.3333 

A*B 2 2 -0.333 

A*B 3 1 0.3333 

A*B 3 2 -0.333 

Type 3 Coefficients for A*B 

Effect A B Row1 Row2 

Intercept 

A 1 

A 2 

A 3 

B 1 

B 2 

A*B 1 1 1 

A*B 1 2 -1 

A*B 2 1 1 

A*B 2 2 -1 

A*B 3 1 -1 -1 

A*B 3 2 1 1

Output 56.9.2 Type 3 Tests in Split-Plot Example 



Num Den 


A 2 6 4.07 0.0764 

B 1 9 19.39 0.0017 

A*B 2 9 4.02 0.0566 

If i: denotes a whole-plot main effect mean, :j denotes a subplot main effect mean, and ij denotes 

a cell mean, the five components shown in Output 56.9.3 correspond to tests of the following: 

H0 W 1: D 2: 

H0 W 2: D 3: 

H0 W :1 D :2 

H0 W 11 12 D 31 32 

H0 W 21 22 D 31 32 

Output 56.9.3 Type 3 L Components Table 

L Components of Type 3 Tests of Fixed Effects 

L Standard 

Effect Index Estimate Error DF t Value Pr > |t| 

A 1 7.1250 3.1672 6 2.25 0.0655 

A 2 8.3750 3.1672 6 2.64 0.0383 

B 1 5.5000 1.2491 9 4.40 0.0017 

A*B 1 7.7500 3.0596 9 2.53 0.0321 

A*B 2 7.2500 3.0596 9 2.37 0.0419 

The first three components are comparisons of marginal means. The fourth component compares 

the effect of factor B at the first whole-plot level against the effect of B at the third whole-plot level. 

Finally, the last component tests whether the factor B effect changes between the second and third 

whole-plot level. 

The Type 3 component tests can also be produced with these corresponding ESTIMATE statements: 


class a b block ; 

model y = a b a*b; 


estimate ’a 1’ a 1 0 -1; 

estimate ’a 2’ a 0 1 -1;


estimate ’b 1’ b 1 -1; 

estimate ’a*b 1’ a*b 1 -1 0 0 -1 1; 

estimate ’a*b 2’ a*b 0 0 1 -1 -1 1; 

ods select Estimates; 

run; 

The results are shown in Output 56.9.4. 

Output 56.9.4 Results from ESTIMATE Statements 


Estimates 

Standard 

Label Estimate Error DF t Value Pr > |t| 

a 1 7.1250 3.1672 6 2.25 0.0655 

a 2 8.3750 3.1672 6 2.64 0.0383 

b 1 5.5000 1.2491 9 4.40 0.0017 

a*b 1 7.7500 3.0596 9 2.53 0.0321 

a*b 2 7.2500 3.0596 9 2.37 0.0419 

A second useful application of the LCOMPONENTS option is in polynomial models, where Type 

1 tests are often used to test the entry of model terms sequentially. The SOLUTION option in the 

MODEL statement displays the regression coefficients that correspond to a Type 3 analysis. That 

is, the coefficients represent the partial coefficients you would get by adding the regressor variable 

last in a model containing all other effects, and the tests are identical to those in the “Type 3 Tests 

of Fixed Effects” table. 

Consider the following DATA step and the fit of a third-order polynomial regression model. 

data polynomial; 

do x=1 to 20; input y@@; output; end; 

datalines; 

1.092 1.758 1.997 3.154 3.880 

3.810 4.921 4.573 6.029 6.032 

6.291 7.151 7.154 6.469 7.137 

6.374 5.860 4.866 4.155 2.711 

; 

proc mixed data=polynomial; 

model y = x x*x x*x*x / s lcomponents htype=1,3; 

run; 

The t tests displayed in the “Solution for Fixed Effects” table are Type 3 tests, sometimes referred 

to as partial tests. They measure the contribution of a regressor in the presence of all other regressor 

variables in the model.

Output 56.9.5 Parameter Estimates in Polynomial Model 




Standard 


Intercept 0.7837 0.3545 16 2.21 0.0420 

x 0.3726 0.1426 16 2.61 0.0189 

x*x 0.04756 0.01558 16 3.05 0.0076 

x*x*x -0.00306 0.000489 16 -6.27 |t| 

x 1 0.1763 0.01259 16 14.01


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Subject Index 

2D geometric anisotropic structure 

MIXED procedure, 3953 

Akaike’s information criterion 

example (MIXED), 4011, 4025, 4054 

MIXED procedure, 3901, 3970, 3991 

Akaike’s information criterion (finite sample 

corrected version) 

MIXED procedure, 3901, 3991 

alpha level 

MIXED procedure, 3899, 3915, 3919, 3923, 

3944 

anisotropic power covariance structure 


anisotropic spatial power structure 


ANTE(1) structure 


ante-dependence structure 


AR(1) structure 


asymptotic covariance 


at sign (@) operator 


autoregressive moving-average structure 


autoregressive structure 

example (MIXED), 4019 


banded Toeplitz structure 


bar (|) operator 


Bayesian analysis 


BLUE 


BLUP 


Bonferroni adjustment 


boundary constraints 


CALIS procedure 

compared to MIXED procedure, 3889 

chi-square test 


class level 


classification variables 


compound symmetry structure 



computational details 


computational problems 

convergence (MIXED), 4005 

conditional residuals 


confidence limits 


constraints 

boundary (MIXED), 3938, 3939 

containment method 


continuous-by-class effects 


continuous-nesting-class effects 


contrasts 


convergence criterion 

MIXED procedure, 3899, 3900, 3990, 4006 

convergence problems 


convergence status 


Cook’s D 


Cook’s D for covariance parameters 


correlation 

estimates (MIXED), 3945, 3947, 3952, 4021 

covariance 

parameter estimates (MIXED), 3900, 3901 

parameter estimates, ratio (MIXED), 3909 

parameters (MIXED), 3886 

covariance parameter estimates 


covariance structure 

anisotropic power (MIXED), 3960 

ante-dependence (MIXED), 3956

autoregressive (MIXED), 3957 

autoregressive moving-average (MIXED), 

3957 

banded (MIXED), 3960 

compound symmetry (MIXED), 3957 

equi-correlation (MIXED), 3957 

examples (MIXED), 3954 

exponential anisotropic (MIXED), 3958 

factor-analytic (MIXED), 3957 

general linear (MIXED), 3958 

heterogeneous autoregressive (MIXED), 

3957 

heterogeneous compound symmetry 

(MIXED), 3957 

heterogeneous Toeplitz (MIXED), 3960 

Huynh-Feldt (MIXED), 3958 

Kronecker (MIXED), 3961 

Matérn (MIXED), 3959 


power (MIXED), 3960 

simple (MIXED), 3958 

spatial geometric anisotropic (MIXED), 

3959 

Toeplitz (MIXED), 3960 

unstructured (MIXED), 3960 

unstructured, correlation (MIXED), 3960 

variance components (MIXED), 3961 

covariance structures 


covariates 


CovRatio 


CovRatio for covariance parameters 


CovTrace 


CovTrace for covariance parameters 


crossed effects 




degrees of freedom 

between-within method (MIXED), 3901, 

3925 

containment method (MIXED), 3924, 3925 

Kenward-Roger method (GLIMMIX), 3927 

method (MIXED), 3924 

MIXED procedure, 3913, 3915, 3920, 3924 

residual method (MIXED), 3925 

Satterthwaite method (MIXED), 3925 

DFFITS 


dimension information 


dimensions 


direct product structure 


Dunnett’s adjustment 


EBLUP 


effect 

name length (MIXED), 3903 

empirical best linear unbiased prediction 


empirical estimator 


estimability 


estimable functions 


estimation 

mixed model (MIXED), 3968 

estimation methods 


examples, MIXED 

ASYCOV matrix, 4017 

asymptotic covariance of covariance 

parameters, 4017 

autoregressive structure, R-side, 4019 

box plots, 4070 

box plots, paneling, 3908 

broad inference space, 3912, 3914 

compound symmetry, G-side setup, 3965, 

4023 

compound symmetry, R-side setup, 3965, 

4020 

constrained anisotropic model, 3958 

covariates in LS-mean construction, 3919 

COVTEST option, 4014, 4026 

deletion estimates, 4055 

doubly repeated measure, 3961 

estimate, with subject, 3915 

fat absorption data, 4055 

fixed-effect solutions, 4041 

full-rank parameterization, 4018 

GDATA= option in RANDOM statement, 

4034 

geometrically anisotropic model, 3959 

getting started, 3890 

GLM procedure, split-plot design, 4011 

graphics, box plots, 4070 

graphics, influence diagnostics, 4055, 4066

graphics, residual panel, 3998 

graphics, studentized residual panel, 3998 

GROUP= effect in RANDOM statement, 

4023 

height data, 3890 

holding covariance parameters fixed, 3938, 

3958, 3959 

IML procedure, reading ASYCOV, 4017 

inference space, broad, 3912, 3914 

inference space, intermediate, 3914 

inference space, narrow, 3912, 3914 

inference spaces, 4012 

influence analysis, iterative, 4055, 4066 

influence analysis, non-iterative, 4065 

influence analysis, set deletion, 4065, 4066 

influence analysis, tuples, 3931 

intermediate inference space, 3914 

known covariance parameters, 3937 

known G and R matrix, 4034 

Kronecker covariance structure, 3961 

L-components, 3933, 4073, 4076 

least squares mean, slice, 3922 

least squares means, AT option, 3919 

least squares means, covariate, 3919 

least squares means, differences against 

control, 3920 

line-source sprinkler data, 4049 

local power-of-mean model, 3950 

maximum likelihood estimation, 4014 

mixed model equations, 4026, 4034 

mixed model equations, solution, 4026, 

4034 

multiple plot requests, 3909 

multiple traits data, 4033 

multivariate analysis, 3961 

narrow inference space, 3912, 3914 

nested error structure, 4045 

nested random effects, 3893 

NOITER option, 3937, 4034 

oven data (Hemmerle and Hartley, 1973), 

4026 

parameter grid search, 4026 

pharmaceutical stability data, 4041 

polynomial model, 4076 

POM data set, 3950 

POM fitting, iterated, 3951 

Pothoff and Roy growth measurements, 

4013, 4065 

random coefficient model, 4019, 4041, 4069 

random-effect solutions, 4041 

residual panel, 3998 

row-wise multiplicity adjustment, 3918 

Satterthwaite method, 3918 

set deletion, 4066 

SGRENDER procedure, 4032 

slice F test, 3922 

spatial power structure, 4054 

specifying lower bounds, 3938 

specifying values for degrees of freedom, 

3924 

split-plot design, 3966, 4009 

split-plot design, data, 4008, 4073 

split-plot design, equivalent model, 4013 

starting values, 4026 

studentized maximum modulus, 3918 

studentized residual panel, 3998 

subject and no-subject formulation, 3965 

subject contrasts, 3915 

subject v. no-subject formulation, 4013 

subject-specific R matrices, 3951 

subject-specific V matrices, 3947 

Toeplitxz structure, 4049 

tuples, influence analysis, 3931 

two-way analysis of variance, 3890 

unstructured covariance, G-side, 3947 

unstructured covariance, R-side, 4014, 4065 

varying covariance parameters, 4023 

exponential covariance structure 


external studentization 


factor analytic structures 


Fisher information matrix 



Fisher’s scoring method 


fixed effects 


fixed-effects parameters 


G matrix 


3964, 4046 

gaussian covariance structure 


general linear covariance structure 


generalized inverse, 3971 


GLM procedure 

compared to other procedures, 3889 

gradient 


grid search


growth curve analysis 


Hannan-Quinn information criterion 


Hessian matrix 


3990, 3991, 4005, 4006, 4017, 4026 

heterogeneity 



heterogeneous 

AR(1) structure (MIXED), 3953 

compound-symmetry structure (MIXED), 

3953 

covariance structures (MIXED), 3962 

Toeplitz structure (MIXED), 3953 

hierarchical model 


Hotelling-Lawley-McKeon statistic 


Hotelling-Lawley-Pillai-Samson statistic 


Hsu’s adjustment 


Huynh-Feldt 

structure (MIXED), 3953 

hypothesis tests 

mixed model (MIXED), 3972, 3992 

inference 


space, mixed model (MIXED), 3911, 3912, 

3914, 4012 

infinite likelihood 


influence diagnostics 


influence diagnostics, details 


influence plots 


information criteria 


initial values 


interaction effects 


intercept 


internal studentization 


intraclass correlation coefficient 


iteration history 


iterations 

history (MIXED), 3990 

Kenward-Roger method 


Kronecker product structure 


L matrices 

mixed model (MIXED), 3911, 3916, 3972 


LATTICE procedure 


least squares means 

Bonferroni adjustment (MIXED), 3918 

BYLEVEL processing (MIXED), 3920 

comparison types (MIXED), 3920 

covariate values (MIXED), 3919 

Dunnett’s adjustment (MIXED), 3918 

examples (MIXED), 4026, 4050 

Hsu’s adjustment (MIXED), 3918 


multiple comparison adjustment (MIXED), 

3917, 3918 

nonstandard weights (MIXED), 3921 

observed margins (MIXED), 3921 

Sidak’s adjustment (MIXED), 3918 

simple effects (MIXED), 3922 

simulation-based adjustment (MIXED), 

3918 

Tukey’s adjustment (MIXED), 3918 

leverage 

MIXED Procedure, 3984 

likelihood distance 


likelihood ratio test, 4011 




linear covariance structure 


log-linear variance model 


main effects 


marginal residuals 


Matérn covariance structure 


matrix 

notation, theory (MIXED), 3962

maximum likelihood estimation 


MDFFITS 


MDFFITS for covariance parameters 


memory requirements 


missing level combinations 


mixed model (MIXED), see also MIXED 

procedure 

estimation, 3968 

formulation, 3963 

hypothesis tests, 3972, 3992 

inference, 3972 

inference space, 3911, 3912, 3914, 4012 

least squares means, 3916 

likelihood ratio test, 3972, 3973 

linear model, 3886 

maximum likelihood estimation, 3969 

notation, 3888 

objective function, 3990 

parameterization, 3975 

predicted values, 3916 

restricted maximum likelihood, 4010 

theory, 3962 

Wald test, 3972, 4017 

mixed model equations 



MIXED Procedure 

leverage, 3984 

PRESS Residual, 3984 

PRESS Statistic, 3984 

MIXED procedure, see also mixed model 

2D geometric anisotropic structure, 3953 

Akaike’s information criterion, 3901, 3970, 

3991 

Akaike’s information criterion (finite sample 

corrected version), 3901, 3991 

alpha level, 3899, 3915, 3919, 3923, 3944 

anisotropic power covariance structure, 3954 

anisotropic spatial power structure, 3954 

ANTE(1) structure, 3953 

ante-dependence structure, 3953 

AR(1) structure, 3953 

ARIMA procedure, compared, 3889 

ARMA structure, 3953 

assumptions, 3886 

asymptotic covariance, 3899 

AUTOREG procedure, compared, 3889 

autoregressive moving-average structure, 

3953 

autoregressive structure, 3953, 4019 

banded Toeplitz structure, 3953 

basic features, 3887 

Bayesian analysis, 3939 

between-within method, 3901, 3925 

BLUE, 3971 

BLUP, 3971, 4040 

Bonferroni adjustment, 3918 

boundary constraints, 3938, 3939, 4005 

BYLEVEL processing of LSMEANS, 3920 

CALIS procedure, compared, 3889 

chi-square test, 3913, 3923 

Cholesky root, 3935, 3982, 4004 

class level, 3903, 3989 

classification variables, 3910 

compound symmetry structure, 3953, 3964, 

4020, 4025 

computational details, 4004 

computational order, 4004 

conditional residuals, 3981 

confidence interval, 3915, 3944 

confidence limits, 3900, 3915, 3920, 3923, 

3944 

containment method, 3924, 3925 

continuous effects, 3945, 3946, 3949, 3952 

continuous-by-class effects, 3978 

continuous-nesting-class effects, 3978 

contrasted SAS procedures, 3889 

contrasts, 3911, 3914 

convergence criterion, 3899, 3900, 3990, 

4006 

convergence problems, 4005 

convergence status, 3990 

Cook’s D, 3985 

Cook’s D for covariance parameters, 3985 

correlation estimates, 3945, 3947, 3952, 

4021 

correlations of least squares means, 3920 

covariance parameter estimates, 3900, 3901, 

3991 

covariance parameter estimates, ratio, 3909 

covariance parameters, 3886 

covariance structure, 3888, 3953, 3954, 

4013 

covariances of least squares means, 3920 

covariate values for LSMEANS, 3919 

covariates, 3976 

CovRatio, 3986 

CovRatio for covariance parameters, 3986 

CovTrace, 3986 

CovTrace for covariance parameters, 3986 

CPU requirements, 4007 

crossed effects, 3977 

default output, 3989

degrees of freedom, 3912–3916, 3920, 3924, 

3936, 3973, 3980, 3992, 4005, 4040 

DFFITS, 3985 

dimension information, 3989 

dimensions, 3902, 3903 

direct product structure, 3953 

Dunnett’s adjustment, 3918 

EBLUPs, 3946, 3971, 4032, 4047 

effect name length, 3903 

empirical best linear unbiased prediction, 

3934 

empirical estimator, 3901 

estimability, 3911, 3913–3916, 3921, 3936, 

3972, 3980 

estimable functions, 3933 

estimation methods, 3902 

exponential covariance structure, 3954 

factor analytic structures, 3953 

Fisher information matrix, 3991, 4026 

Fisher’s scoring method, 3899, 3909, 4006 

fitting information, 3991, 3992 

fixed effects, 3888 

fixed-effects parameters, 3886, 3936, 3963 

fixed-effects variance matrix, 3936 

function evaluations, 3902 

G matrix, 3888, 3943, 3944, 3963, 3964, 

4046 

gaussian covariance structure, 3954 

general linear covariance structure, 3953 

generalized inverse, 3913, 3971 

GLIMMIX procedure, compared, 3890 

gradient, 3900, 3990 

grid search, 3937, 4026 

growth curve analysis, 3964 

Hannan-Quinn information criterion, 3901 

Hessian matrix, 3899, 3900, 3909, 3938, 

3990, 3991, 4005, 4006, 4017, 4026 

heterogeneity, 3945, 3949, 4023 

heterogeneous AR(1) structure, 3953 

heterogeneous compound-symmetry 

structure, 3953 

heterogeneous covariance structures, 3962 

heterogeneous Toeplitz structure, 3953 

hierarchical model, 4041 

Hotelling-Lawley-McKeon statistic, 3949 

Hotelling-Lawley-Pillai-Sampson statistic, 

3949 

Hsu’s adjustment, 3918 

Huynh-Feldt structure, 3953 

infinite likelihood, 3948, 4005, 4006 

influence diagnostics, 3932, 3982 

influence plots, 4000 

information criteria, 3901 

initial values, 3937 

input data sets, 3901 

interaction effects, 3977 

intercept, 3976 

intercept effect, 3934, 3943 

intraclass correlation coefficient, 4021 

introductory example, 3890 

iteration history, 3990 

iterations, 3902, 3990 

Kenward-Roger method, 3927 

Kronecker product structure, 3953 

LATTICE procedure, compared, 3889 

least squares means, 3920, 4026, 4050 

leave-one-out-estimates, 4000 

likelihood distance, 3987 

likelihood ratio test, 3992 

linear covariance structure, 3953 

log-linear variance model, 3950 

main effects, 3976 

marginal residuals, 3981 

Matérn covariance structure, 3953 

matrix notation, 3962 

MDFFITS, 3985 

MDFFITS for covariance parameters, 3986 

memory requirements, 4007 

missing level combinations, 3980 

mixed linear model, 3886 

mixed model, 3963 

mixed model equations, 3903, 3970, 4026 

mixed model theory, 3962 

model information, 3903, 3989 

model selection, 3970 

multilevel model, 4041 

multiple comparisons of least squares 

means, 3917, 3918, 3920 

multiple tables, 3995 

multiplicity adjustment, 3917 

multivariate tests, 3949 

nested effects, 3977 

nested error structure, 4045 

NESTED procedure, compared, 3889 

Newton-Raphson algorithm, 3969 

non-full-rank parameterization, 3889, 3950, 

3980 

nonstandard weights for LSMEANS, 3921 

nugget effect, 3950 

number of observations, 3989 

oblique projector, 3984 

observed margins for LSMEANS, 3921 

ODS graph names, 4002 

ODS Graphics, 3905, 3998 

ODS table names, 3993 

ordering of effects, 3904, 3979 

over-parameterization, 3976 

parameter constraints, 3938, 4005

parameterization, 3975 

Pearson residual, 3935 

pharmaceutical stability, example, 4041 

plotting the likelihood, 4032 

polynomial effects, 3976 

power-of-the-mean model, 3950 

predicted means, 3935 

predicted value confidence intervals, 3923 

predicted values, 3934, 4026 

prior density, 3940 

profiling residual variance, 3904, 3938, 

3950, 3969, 4004 

R matrix, 3888, 3948, 3951, 3963, 3964 

random coefficients, 4019, 4041 

random effects, 3888, 3943 

random-effects parameters, 3887, 3946, 

3963 

regression effects, 3976 

rejection sampling, 3941 

repeated measures, 3887, 3948, 4013 

residual diagnostics, details, 3980 

residual method, 3925 

residual plots, 3998 

residual variance tolerance, 3935 

restricted maximum likelihood (REML), 

3887 

ridging, 3909, 3969 

sandwich estimator, 3901 

Satterthwaite method, 3925 

scaled residual, 3936, 3982 

Schwarz’s Bayesian information criterion, 

3901, 3970, 3991 

scoring, 3899, 3909, 4006 

Sidak’s adjustment, 3918 

simple effects, 3922 

simulation-based adjustment, 3918 

singularities, 4006 

spatial anisotropic exponential structure, 

3953 

spatial covariance structure, 3954, 3962, 

4005 

split-plot design, 3966, 4008 

standard linear model, 3888 

statement positions, 3896 

studentized residual, 3935, 3985 

subject effect, 3912, 3946, 3952, 4007, 4013 

summary of commands, 3897 

sweep operator, 3985, 4004 

table names, 3993 

test components, 3933 

Toeplitz structure, 3953, 4050 

TSCSREG procedure, compared, 3889 

Tukey’s adjustment, 3918 

Type 1 estimation, 3902 

Type 1 testing, 3928 


Type 2 testing, 3928 


Type 3 testing, 3928, 3992 

unstructured correlations, 3953 

unstructured covariance matrix, 3953 

unstructured R matrix, 3952 

V matrix, 3947 

VARCOMP procedure, example, 4026 

variance components, 3887, 3953 

variance ratios, 3938, 3946 

Wald test, 3991, 3992 

weighted LSMEANS, 3921 

weighting, 3962 

zero design columns, 3927 

zero variance component estimates, 4005 

model 

information (MIXED), 3903 

model information 


model selection 


multilevel model 


multiple comparison adjustment (MIXED) 

least squares means, 3917, 3918 

multiple comparisons of least squares means 


multiple tables 


multiplicity adjustment 


row-wise (MIXED), 3917 

multivariate tests 


nested effects 


nested error structure 


NESTED procedure 

compared to other procedures, 3889 

Newton-Raphson algorithm 


non-full-rank parameterization 


nugget effect 


number of observations 


objective function 

mixed model (MIXED), 3990

oblique projector 


ODS graph names 


ODS Graphics 


options summary 

LSMEANS statement, (MIXED), 3916 

MODEL statement (MIXED), 3922 

PROC MIXED statement, 3898 

RANDOM statement (MIXED), 3943 

REPEATED statement (MIXED), 3948 

over-parameterization 


parameter constraints 


parameterization 



Pearson residual 


pharmaceutical stability 


plots 

likelihood (MIXED), 4032 

polynomial effects 


power-of-the-mean model 


predicted means 


predicted value confidence intervals 


predicted values 




PRESS residual 


PRESS statistic 


prior density 


profiling residual variance 


R matrix 


3964 

random coefficients 

example (MIXED), 4019, 4041 

random effects 


random-effects parameters 


regression effects 


rejection sampling 


REML, see restricted maximum likelihood 

repeated measures 


residual maximum likelihood, see also restricted 

maximum likelihood 

residual maximum likelihood (REML) 


residual plots 


residuals, details 


restricted maximum likelihood 


ridging 


sandwich estimator 


Satterthwaite method 


scaled residual 


Schwarz’s Bayesian information criterion 



scoring 


Sidak’s adjustment 


simple effects 


simulation-based adjustment 


singularities 


spatial anisotropic exponential structure 


spatial covariance structure 



split-plot design 


standard linear model 


studentized residual 

external, 3985 

internal, 3985 

MIXED procedure, 3935, 3985

subject effect 


4013 

summary of commands 


table names 


test components 


Toeplitz structure 



Tukey’s adjustment 


Type 1 estimation 


Type 1 testing 










unstructured correlations 


unstructured covariance matrix 


V matrix 


VARCOMP procedure 



variance components 


variance ratios 


Wald test 



weighting 


zero variance component estimates 

MIXED procedure, 4005

Syntax Index 

ABSOLUTE option 

PROC MIXED statement, 3899, 3990 

ADJDFE= option 

LSMEANS statement (MIXED), 3917 

ADJUST= option 


ALG= option 

PRIOR statement (MIXED), 3941 

ALPHA= option 

ESTIMATE statement (MIXED), 3915 




ALPHAP= option 


ANOVAF option 


ASYCORR option 


ASYCOV option 


AT MEANS option 


AT option 

LSMEANS statement (MIXED), 3919, 3920 

BDATA= option 


BY statement 


BYLEVEL option 


CHISQ option 

CONTRAST statement (MIXED), 3913 


CL option 





CL= option 


CLASS statement 


CONTAIN option 

MODEL statement (MIXED), 3924, 3925 

CONTRAST statement 


CONVF option 


CONVG option 


CONVH option 


CORR option 


CORRB option 


COV option 


COVB option 


COVBI option 


COVTEST option 


DATA= option 



DDF= option 


DDFM= option 


DF= option 




DFBW option 


DIFF option 


DIVISOR= option 


E option 





E1 option 


E2 option 


E3 option 


EFFECT= modifier

INFLUENCE option, MODEL statement 


EMPIRICAL option 

MIXED, 3901 

EQCONS= option 

PARMS statement (MIXED), 3938 

ESTIMATE statement 


ESTIMATES modifier 



FLAT option 


FULLX option 


G option 


GC option 


GCI option 


GCORR option 


GDATA= option 


GI option 


GRID= option 


GRIDT= option 


GROUP option 



GROUP= option 



HLM option 


HLPS option 


HOLD= option 


HTYPE= option 


IC option 


ID statement 


IFACTOR= option 


INFLUENCE option 


INFO option 


INTERCEPT option 


ITDETAILS option 


ITER= modifier 



JEFFREYS option 


KEEP= modifier 



LCOMPONENTS option 


LDATA= option 



LOCAL= option 


LOCALW option 


LOGDETH option 


LOGNOTE option 


LOGNOTE= option 


LOGRBOUND= option 


LOWERB= option 


LOWERTAILED option 


LSMEANS statement 


MAXFUNC= option 


MAXITER= option 


METHOD= option 



INFLUENCE option, 3928 

syntax, 3896 

MIXED procedure, BY statement, 3910 

MIXED procedure, CLASS statement, 3910, 

3989

TRUNCATE option, 3911 

MIXED procedure, CONTRAST statement, 3911 

CHISQ option, 3913 

DF= option, 3913 

E option, 3913 

GROUP option, 3913 

SINGULAR= option, 3914 

SUBJECT option, 3914 

MIXED procedure, ESTIMATE statement, 3914 

ALPHA= option, 3915 

CL option, 3915 


DIVISOR= option, 3915 


GROUP option, 3915 

LOWERTAILED option, 3915 


SUBJECT option, 3915 

UPPERTAILED option, 3916 

MIXED procedure, ID statement, 3916 

MIXED procedure, LSMEANS statement, 3916, 

4026 

ADJUST= option, 3918 


AT MEANS option, 3919 

AT option, 3919, 3920 

BYLEVEL option, 3920, 3921 


CORR option, 3920 

COV option, 3920 


DIFF option, 3920 


OBSMARGINS option, 3921 

PDIFF option, 3920, 3921 


SLICE= option, 3922 

MIXED procedure, MODEL statement, 3922 

ALPHAP= option, 3923 

CHISQ option, 3923 


CONTAIN option, 3924, 3925 

CORRB option, 3924 

COVB option, 3924 

COVBI option, 3924 

DDF= option, 3924 

DDFM= option, 3924 


E1 option, 3927 



FULLX option, 3919, 3927 

HTYPE= option, 3928 

INFLUENCE option, 3928 

INTERCEPT option, 3933 

LCOMPONENTS option, 3933 

NOCONTAIN option, 3934 

NOINT option, 3934, 3976 

NOTEST option, 3934 

ORDER= option, 3980 

OUTP= option, 4026 

OUTPRED= option, 3934 

OUTPREDM= option, 3935 

RESIDUAL option, 3935, 3982 

SINGCHOL= option, 3935 

SINGRES= option, 3935 


SOLUTION option, 3936, 3980 

VCIRY option, 3936, 3982 

XPVIX option, 3936 

XPVIXI option, 3936 

ZETA= option, 3936 

MIXED procedure, MODEL statement, 

INFLUENCE option 

EFFECT=, 3929 

ESTIMATES, 3929 

ITER=, 3929 

KEEP=, 3930 

SELECT=, 3930 

SIZE=, 3931 

MIXED procedure, PARMS statement, 3937, 

4026 

EQCONS= option, 3938 

HOLD= option, 3938 

LOGDETH option, 3938 

LOWERB= option, 3938 

NOBOUND option, 3938 

NOITER option, 3938 

NOPROFILE option, 3938 

OLS option, 3939 

PARMSDATA= option, 3939 

PDATA= option, 3939 

RATIOS option, 3939 

UPPERB= option, 3939 

MIXED procedure, PRIOR statement, 3939 

ALG= option, 3941 

BDATA= option, 3941 

DATA= option, 3940 

FLAT option, 3941 

GRID= option, 3941 

GRIDT= option, 3941 

IFACTOR= option, 3941 

JEFFREYS option, 3941 

LOGNOTE= option, 3941 

LOGRBOUND= option, 3941 

NSAMPLE= option, 3942 

NSEARCH= option, 3942 

OUT= option, 3942

OUTG= option, 3942 

OUTGT= option, 3942 

PSEARCH option, 3942 

PTRANS option, 3942 

SEED= option, 3942 

SFACTOR= option, 3942 

TDATA= option, 3943 

TRANS= option, 3943 

UPDATE= option, 3943 

MIXED procedure, PROC MIXED statement, 

3898 

ABSOLUTE option, 3899, 3990 


ANOVAF option, 3899 

ASYCORR option, 3899 

ASYCOV option, 3899, 4026 

CL= option, 3900 

CONVF option, 3900, 3990 

CONVG option, 3900, 3990 

CONVH option, 3900, 3990 

COVTEST option, 3901, 3991 

DATA= option, 3901 

DFBW option, 3901 

IC option, 3901 

INFO option, 3902 

ITDETAILS option, 3902 

LOGNOTE option, 3902 

MAXFUNC= option, 3902 

MAXITER= option, 3902 

METHOD= option, 3902, 4014 

MMEQ option, 3903, 4026 

MMEQSOL option, 3903, 4026 

NAMELEN= option, 3903 

NOBOUND option, 3903 

NOCLPRINT option, 3903 

NOINFO option, 3903 

NOITPRINT option, 3903 

NOPROFILE option, 3904, 3969 

ORD option, 3904 

ORDER= option, 3904, 3976 

PLOT option, 3905 

PLOTS option, 3905 

RATIO option, 3909, 3991 

RIDGE= option, 3909 

SCORING= option, 3909 

SIGITER option, 3909 

UPDATE option, 3909 

MIXED procedure, RANDOM statement, 3889, 

3943, 4008 



G option, 3944 

GC option, 3944 

GCI option, 3944 

GCORR option, 3945 

GDATA= option, 3945 

GI option, 3945 

GROUP= option, 3945 

LDATA= option, 3945 

NOFULLZ option, 3946 

RATIOS option, 3946 

SOLUTION option, 3946 

SUBJECT= option, 3912, 3946 

TYPE= option, 3946 

V option, 3947 

VC option, 3947 

VCI option, 3947 

VCORR option, 3947 

VI option, 3947 

MIXED procedure, REPEATED statement, 3889, 

3948, 4013 

GROUP= option, 3949 

HLM option, 3949 

HLPS option, 3949 

LDATA= option, 3950 

LOCAL= option, 3950 

LOCALW option, 3951 

NONLOCALW option, 3951 

R option, 3951 

RC option, 3952 

RCI option, 3952 

RCORR option, 3952 

RI option, 3952 

SSCP option, 3952 

SUBJECT= option, 3952 

TYPE= option, 3953 

MIXED procedure, WEIGHT statement, 3962 

MMEQ option 


MMEQSOL option 


MODEL statement 


Modifiers of INFLUENCE option 


NAMELEN= option 


NOBOUND option 



NOCLPRINT option 


NOCONTAIN option 


NOFULLZ option 


NOINFO option


NOINT option 


NOITER option 


NOITPRINT option 


NONLOCALW option 


NOPROFILE option 



NOTEST option 


NSAMPLE= option 


NSEARCH= option 


OBSMARGINS option 


OLS option 


ORD option 


ORDER= option 



OUT= option 


OUTG= option 


OUTGT= option 


OUTP= option 


OUTPRED= option 


OUTPREDM= option 


PARMS statement 


PARMSDATA= option 


PDATA= option 


PDIFF option 


PLOT option 


PLOTS option 


PRIOR statement 


PROC MIXED statement, see MIXED procedure 

PSEARCH option 


PTRANS option 


R option 


RANDOM statement 


RATIO option 


RATIOS option 



RC option 


RCI option 


RCORR option 


REPEATED statement 


RESIDUAL option 


3982 


RI option 


RIDGE= option 


SCORING= option 


SEED= option 


SELECT= modifier 



SFACTOR= option 


SIGITER option 


SINGCHOL= option 


SINGRES= option 


SINGULAR= option 





SIZE= modifier



SLICE= option 


SOLUTION option 



SSCP option 


SUBJECT option 



SUBJECT= option 

RANDOM statement (MIXED), 3912, 3946 


TDATA= option 


TRANS= option 


TYPE= option 



UPDATE option 


UPDATE= option 


UPPERB= option 


UPPERTAILED option 


V option 


VC option 


VCI option 


VCIRY option 


3982 


VCORR option 


VI option 


WEIGHT statement 


XPVIX option 


XPVIXI option 


ZETA= option 

MODEL statement (MIXED), 3936

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SAS/STAT 9.2 User's Guide: The MIXED Procedure (Book Excerpt)

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