OUTPUT
<OUT=SASdataset>
<keyword<(keywordoptions)> <=name>> …
<keyword<(keywordoptions)> <=name>> </ options> ;
The OUTPUT statement creates a data set that contains predicted values and residual diagnostics, computed after fitting the model. By default, all variables in the original data set are included in the output data set.
You can use the ID statement to select a subset of the variables from the input data set to be added to the output data set.
For example, suppose that the data set Scores
contains the variables score
, machine
, and person
. The following statements fit a model with fixed machine and random person effects and save the predicted and residual values
to the data set igausout
:
proc hpmixed data = Scores; class machine person score; model score = machine; random person; output out=igausout pred=p resid=r; run;
You can specify the following options in the OUTPUT statement before the slash (/).
specifies the name of the output data set. If the OUT= option is omitted, the procedure uses the DATAn
convention to name the output data set.
specifies a statistic to include in the output data set and optionally assigns the variable the name name
. You can use the keywordoptions to control which type of a particular statistic to compute. The keywordoptions can take on the following values:
uses the predictors of the random effects in computing the statistic.
does not use the predictors of the random effects in computing the statistic.
The default is to compute statistics by using BLUPs. For example, the following two OUTPUT statements are equivalent:
output out=out1 pred=predicted lcl=lower; output out=out1 pred(blup)=predicted lcl(blup)=lower;
If a particular combination of keyword and keyword options is not supported, the statistic is not computed and a message is produced in the SAS log.
A keyword can appear multiple times in the OUTPUT statement. Table 46.7 lists the keywords and the default names assigned by the HPMIXED procedure if you do not specify a name. In this table, y denotes the response variable.
Table 46.7: Keywords for Output Statistics
Keyword 
Options 
Description 
Expression 
Name 

PREDICTED 
BLUP 
Linear predictor 

Pred 
NOBLUP 
Marginal linear predictor 

PredPA 

STDERR 
BLUP 
Standard deviation of linear predictor 

StdErr 
NOBLUP 
Standard deviation of marginal linear predictor 

StdErrPA 

RESIDUAL 
BLUP 
Residual 

Resid 
NOBLUP 
Marginal residual 

ResidPA 

PEARSON 
BLUP 
Pearsontype residual 

Pearson 
NOBLUP 
Marginal Pearsontype residual 

PearsonPA 

STUDENT 
BLUP 
Studentized residual 

Student 
NOBLUP 
Studentized marginal residual 

StudentPA 

LCL 
BLUP 
Lower prediction limit for linear predictor 
LCL 

NOBLUP 
Lower confidence limit for marginal linear predictor 
LCLPA 

UCL 
BLUP 
Upper prediction limit for linear predictor 
UCL 

NOBLUP 
Upper confidence limit for marginal linear predictor 
UCLPA 

VARIANCE 
BLUP 
Conditional variance of response variable 

Variance 

NOBLUP 
Marginal variance of response variable 

VariancePA 
You can use the following shortcuts to request statistics: PRED for PREDICTED, STD for STDERR, RESID for RESIDUAL, VAR for VARIANCE.
You can specify the following options of the OUTPUT statement after the slash (/).
requests that all statistics are computed. If you do not use a keyword to assign a name, the HPMIXED procedure uses the default name.
determines the coverage probability for twosided confidence and prediction intervals. The coverage probability is computed as . The value of number must be between 0 and 1 inclusively; the default is 0.05.
requests that records from the input data set be written to the output data only for those observations that were used in the analysis. By default, the HPMIXED procedure produces output statistics for all observations in the input data set.
requests that names not be made unique in the case of naming conflicts. By default, the HPMIXED procedure avoids naming conflicts by assigning a unique name to each output variable. If you specify the NOUNIQUE option, variables with conflicting names are not renamed. In that case, the first variable added to the output data set takes precedence.
requests that variables from the input data set not be added to the output data set. This option ignores ID statement but does not apply to variables listed in a BY statement.