Output Data Sets
OUT= Data Set
For each population, the OUT= data set contains the
observed and predicted values of the response functions,
their standard errors, the residuals, and variables
that describe the population and response profiles.
In addition, if you use the standard response functions,
the data set includes observed and predicted values
for the cell frequencies or the cell probabilities,
together with their standard errors and residuals.
See Example 22.11 for an
example of creating an OUT= data set.
Number of Observations
For the standard response functions, there are
s ×(2q-1) observations in the data set for each
BY group, where s is the number of populations, and
q is the number of response functions per population.
Otherwise, there are s ×q observations
in the data set for each BY group.
Variables in the OUT= Data Set
The data set contains the following variables:
- BY variables
- If you use a BY statement, the BY variables
are included in the OUT= data set.
- dependent variables
- If the response functions are the default ones (generalized
logits), then the dependent variables, which describe the
response profiles, are included in the OUT= data set.
When _TYPE_=FUNCTION, the values
of these variables are missing.
- independent variables
- The independent variables, which describe the
population profiles, are included in the OUT= data set.
- _NUMBER_
- the sequence number of the response function or
the cell probability or the cell frequency
- _OBS_
- the observed value
- _PRED_
- the predicted value
- _RESID_
- the residual (observed - predicted)
- _SAMPLE_
- the population number.
This matches the sample number in
the Population Profile section of the output.
- _SEOBS_
- the standard error of the observed value
- _SEPRED_
- the standard error of the predicted value
- _TYPE_
- specifies a character variable with three possible values.
When _TYPE_=FUNCTION, the observed and predicted values
are values of the response functions. When _TYPE_=PROB,
they are values of the cell probabilities.
When _TYPE_=FREQ, they are values of the cell frequencies.
Cell probabilities or frequencies are provided only when the
default response functions are modeled. In this case, cell
probabilities are provided by default, and cell frequencies
are provided if you specify the option PRED=FREQ.
OUTEST= Data Set
This TYPE=EST output data set contains the estimated
parameter vector and its estimated covariance matrix.
If you specify both the ML and WLS options in the MODEL
statement, the OUTEST= data set contains both sets of estimates.
For each BY group, there are p+1 observations
in the data set for each estimation method,
where p is the number of estimated parameters.
The data set contains the following variables:
- B1, B2, and so on
- variables for the estimated parameters.
The OUTEST= data set contains one
variable for each estimated parameter.
- BY variables
- If you use a BY statement, the BY variables
are included in the OUT= data set.
- _METHOD_
- the method used to obtain parameter estimates.
For weighted least-squares estimation, _METHOD_=WLS,
and for maximum likelihood estimation, _METHOD_=ML.
- _NAME_
- identifies parameter names.
When _TYPE_=PARMS, _NAME_ is blank, but when
_TYPE_=COV, _NAME_ has one of the values B1,
B2, and so on, corresponding to the parameter names.
- _STATUS_
- indicates whether the estimates have converged
- _TYPE_
- identifies the statistics contained in the variables
for parameter estimates (B1, B2, and so on).
When _TYPE_=PARMS, the variables contain parameter estimates;
when _TYPE_=COV, they contain covariance estimates.
The variables _METHOD_, _NAME_, and
_TYPE_ are character variables; the BY variables can be
either character or numeric; and the variables for estimated
parameters are numeric.
See Appendix A, "Special SAS Data Sets," for more information on special SAS
data sets.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
