Statistical procedures use ODS Graphics to create graphs as part of their output. ODS Graphics is described in detail in Chapter 21: Statistical Graphics Using ODS.
Before you create graphs, ODS Graphics must be enabled (for example, by specifying the ODS GRAPHICS ON statement). For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21: Statistical Graphics Using ODS.
The overall appearance of graphs is controlled by ODS styles. Styles and other aspects of using ODS Graphics are discussed in the section A Primer on ODS Statistical Graphics in Chapter 21: Statistical Graphics Using ODS.
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 77.28, along with the required statements and options.
Table 77.28: Graphs Produced by PROC MIXED
ODS Graph Name 
Plot Description 
Statement or Option 

Box plots 
PLOTS= BOXPLOT 

CovRatioPlot 
CovRatio statistics for fixed effects or covariance parameters 

CooksDPlot 
Cook’s D for fixed effects or covariance parameters 

Likelihood or restricted likelihood distance 

Panel of deletion estimates 
MODEL
/ INFLUENCE
(EST
) 

InfluenceEstPlot 
Parameter estimates after removing observation or sets of observations 

Panel of influence statistics 

PearsonBoxPlot 
Box plot of Pearson residuals 
PLOTS= PEARSONPANEL(UNPACK BOX) 
PearsonByPredicted 
Pearson residuals vs. predicted 
PLOTS= PEARSONPANEL(UNPACK) 
PearsonHistogram 
Histogram of Pearson residuals 
PLOTS= PEARSONPANEL(UNPACK) 
PearsonPanel 
Panel of Pearson residuals 

PearsonQQplot 
QQ plot of Pearson residuals 
PLOTS= PEARSONPANEL(UNPACK) 
PressPlot 
Plot of PRESS residuals or PRESS statistic 

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 

ResidualQQplot 
QQ 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 
QQ 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 of studentized residuals 
PLOTS= STUDENTPANEL(UNPACK) 
Panel of studentized residuals 

StudentQQplot 
QQ plot of studentized residuals 
PLOTS= STUDENTPANEL(UNPACK) 
VCIRYPanel 
Panel of scaled residuals 
When ODS Graphics is enabled, the LSMESTIMATE and SLICE statements can produce plots that are associated with their analyses. For information about these plots, see the sections LSMESTIMATE Statement and SLICE Statement in Chapter 19: Shared Concepts and Topics.
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 QQ plot, and summary residual and fit statistics (Figure 77.15). 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).
Recall the example in the section Getting Started: MIXED Procedure. The following statements generate several panels of residual graphs:
ods graphics on; proc mixed data=heights plots=studentpanel(marginal conditional); class Family Gender; model Height = Gender / residual; random Family Family*Gender; run; ods graphics off;
The graphs are created when ODS Graphics is enabled. The panel of the studentized marginal residuals is shown in Figure 77.15, and the panel of the studentized conditional residuals is shown in Figure 77.16.
Figure 77.15: Panel of the Studentized (Marginal) Residuals
Since the fixedeffects 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 lowerright 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 77.16: Panel of the Conditional Studentized Residuals
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 77.17 and Figure 77.18. 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 77.8. 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 Figure 77.17 that person 20 has considerable impact on the covariance parameter estimates and moderate influence on the fixedeffects
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.
Figure 77.18 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 77.17: Influence Diagnostics
Figure 77.18: Deletion Estimates