You can use the Output Delivery System (ODS) to create a SAS data set from any piece of PROC SURVEYREG output. See the section ODS Table Names for more information. For a more detailed description of using ODS, see ChapterĀ 20: Using the Output Delivery System.
PROC SURVEYREG also provides an OUTPUT statement to create a data set that contains estimated linear predictors and their standard error estimates, the residuals from the linear regression, and the confidence limits for the predictors.
If you use BRR or jackknife variance estimation, PROC SURVEYREG provides an output data set that stores the replicate weights and an output data set that stores the jackknife coefficients for jackknife variance estimation.
The OUTPUT statement produces an output data set that contains the following:
all original data from the SAS data set input to PROC SURVEYREG
the new variables corresponding to the diagnostic measures specified with statistics keywords in the OUTPUT statement (PREDICTED=, RESIDUAL=, and so on)
When any independent variable in the analysis (including all classification variables) is missing for an observation, then all new variables that correspond to diagnostic measures are missing for the observation in the output data set.
When a dependent variable in the analysis is missing for an observation, then the residual variable that corresponds to R is also missing in the output data set. However, the variables corresponding to LCLM, P, STDP, and UCLM are not missing.
If you specify the OUTWEIGHTS= method-option for VARMETHOD=BRR or VARMETHOD=JACKKNIFE , PROC SURVEYREG stores the replicate weights in an output data set. The OUTWEIGHTS= output data set contains all observations from the DATA= input data set that are valid (used in the analysis). (A valid observation is an observation that has a positive value of the WEIGHT variable. Valid observations must also have nonmissing values of the STRATA and CLUSTER variables, unless you specify the MISSING option.)
The OUTWEIGHTS= data set contains the following variables:
all variables in the DATA= input data set
RepWt_1
, RepWt_2
, , RepWt_n
, which are the replicate weight variables
where n is the total number of replicates in the analysis. Each replicate weight variable contains the replicate weights for the corresponding replicate. Replicate weights equal zero for those observations not included in the replicate.
After the procedure creates replicate weights for a particular input data set and survey design, you can use the OUTWEIGHTS= method-option to store these replicate weights and then use them again in subsequent analyses, either in PROC SURVEYREG or in the other survey procedures. You can use the REPWEIGHTS statement to provide replicate weights for the procedure.
If you specify the OUTJKCOEFS= method-option for VARMETHOD=JACKKNIFE , PROC SURVEYREG stores the jackknife coefficients in an output data set. The OUTJKCOEFS= output data set contains one observation for each replicate. The OUTJKCOEFS= data set contains the following variables:
Replicate
, which is the replicate number for the jackknife coefficient
JKCoefficient
, which is the jackknife coefficient
DonorStratum
, which is the stratum of the PSU that was deleted to construct the replicate, if you specify a STRATA
statement
After the procedure creates jackknife coefficients for a particular input data set and survey design, you can use the OUTJKCOEFS= method-option to store these coefficients and then use them again in subsequent analyses, either in PROC SURVEYREG or in the other survey procedures. You can use the JKCOEFS= option in the REPWEIGHTS statement to provide jackknife coefficients for the procedure.