- OUTPUT <OUT=SAS-data-set>
The OUTPUT statement creates a new SAS data set
containing statistics calculated after fitting the model.
At least one specification of the form
keyword=name is required.
All variables in the original data set are
included in the new data set, along with the
variables created as options to the OUTPUT statement.
These new variables contain fitted values and estimated quantiles.
If you want to create a permanent SAS data set, you must specify
a two-level name (refer to SAS Language Reference: Concepts
for more information on permanent SAS data sets).
Each OUTPUT statement applies to the preceding MODEL statement.
See Example 36.1 for
illustrations of the OUTPUT statement.
The following specifications can appear in the OUTPUT statement:
specifies the new data set.
By default, the procedure uses the DATAn
convention to name the new data set.
specifies the statistics to include in the output
data set and gives names to the new variables.
Specify a keyword for each desired statistic
(see the following list of keywords), an equal
sign, and the variable to contain the statistic.
The keywords allowed and the statistics
they represent are as follows:
specifies an indicator variable to signal censoring.
The variable takes on the value 1 if the
observation is censored; otherwise, it is 0.
specifies a variable to contain the estimates of the cumulative
distribution function evaluated at the observed response.
See the "Predicted Values" section for more information.
specifies a variable in the input data set
to control the estimation of quantiles.
See Example 36.1 for an illustration.
If the specified variable has the value of 1, estimates
for all the values listed in the QUANTILE= list
are computed for that observation in the input
data set; otherwise, no estimates are computed.
If no CONTROL= variable is specified, all
quantiles are estimated for all observations.
If the response variable in the MODEL statement
is binomial, then this option has no effect.
- PREDICTED | P
specifies a variable to contain the quantile estimates.
If the response variable in the corresponding model
statement is binomial, then this variable contains the
estimated probabilities, 1-F(-x'b).
- QUANTILES | QUANTILE | Q
gives a list of values for which quantiles are calculated.
The values must be between 0 and 1, noninclusive.
For each value, a corresponding quantile is estimated.
This option is not used if the response variable
in the corresponding MODEL statement is binomial.
The QUANTILES option can be specified as follows.
Type of List
|list separated by blanks|| |
.2 .4 .6 .8
|list separated by commas|| |
|x to y|| |
.2 to .8
|x to y by z|| |
.2 to .8 by .1
|combination of methods|| |
.1,.2 to .8 by .2
By default, QUANTILES=0.5.
When the response is not binomial, a numeric
variable, _PROB_, is added to the OUTPUT data
set whenever the QUANTILES= option is specified.
The variable _PROB_ gives the probability
value for the quantile estimates.
These are the values taken from the QUANTILES= list and are
given as values between 0 and 1, not as values between 0 and 100.
- STD_ERR | STD
specifies a variable to contain the estimates of the standard
errors of the estimated quantiles or x'b.
If the response used in the MODEL statement
is a binomial response, then these are the
standard errors of x'b.
Otherwise, they are the standard errors of the quantile estimates.
These estimates can be used to compute
confidence intervals for the quantiles.
However, if the model is fit to the log of the event time,
better confidence intervals can usually be computed by
transforming the confidence intervals for the log response.
See Example 36.1 for such a transformation.
specifies a variable to contain the computed value of
x'b, where x is the covariate
vector and b is the vector of parameter estimates.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.