The OUTPUT statement creates a new SAS data set that contains all the variables in the input data set and, optionally, the estimated linear predictors and their standard error estimates, the estimates of the cumulative or individual response probabilities, and the confidence limits for the cumulative probabilities. Formulas for the statistics are given in the section Linear Predictor, Predicted Probability, and Confidence Limits.
If you use the singletrial syntax, the data set also contains a variable named _LEVEL_
, which indicates the level of the response that the given row of output is referring to. For example, the value of the cumulative
probability variable is the probability that the response variable is as large as the corresponding value of _LEVEL_
. For details, see the section OUT= Data Set in the OUTPUT Statement.
The estimated linear predictor, its standard error estimate, all predicted probabilities, and the confidence limits for the cumulative probabilities are computed for all observations in which the explanatory variables have no missing values, even if the response is missing. By adding observations with missing response values to the input data set, you can compute these statistics for new observations, or for settings of the explanatory variables not present in the data, without affecting the model fit.
Table 98.8 summarizes the options available in the OUTPUT statement.
Table 98.8: OUTPUT Statement Options
Option 
Description 

Sets the level of significance 

Names the variable that contains the lower confidence limits 

Names the output data set 

Names the variable that contains the predicted probabilities 

Requests predicted probabilities 

Names the variable that contains the standard error estimates 

Names the variable that contains the upper confidence limits 

Names the variable that contains the estimates of the linear predictor 
You can specify the following options in the OUTPUT statement:
You can specify the following option in the OUTPUT statement after a slash (/):
You can request any of the three given types of predicted probabilities. For example, you can request both the individual predicted probabilities and the cross validated probabilities by specifying PREDPROBS=(I X).
When you specify the PREDPROBS= option, two automatic variables _FROM_
and _INTO_
are included for the singletrial syntax and only one variable, _INTO_
, is included for the events/trials syntax. The _FROM_
variable contains the formatted value of the observed response. The variable _INTO_
contains the formatted value of the response level with the largest individual predicted probability.
If you specify PREDPROBS=INDIVIDUAL, the OUTPUT data set contains k additional variables representing the individual probabilities, one for each response level, where k is the maximum number of response levels across all BY groups. The names of these variables have the form IP_
xxx, where xxx represents the particular level. The representation depends on the following situations:
If you specify the events/trials syntax, xxx is either Event or Nonevent. Thus, the variable that contains the event probabilities is named IP_Event
and the variable containing the nonevent probabilities is named IP_Nonevent
.
If you specify the singletrial syntax with more than one BY group, xxx is 1 for the first ordered level of the response, 2 for the second ordered level of the response, and so forth, as given
in the "Response Profile" table. The variable that contains the predicted probabilities Pr(Y
=1) is named IP_1
, where Y
is the response variable. Similarly, IP_2
is the name of the variable containing the predicted probabilities Pr(Y
=2), and so on.
If you specify the singletrial syntax with no BYgroup processing, xxx is the leftjustified formatted value of the response level (the value can be truncated so that IP_
xxx does not exceed 32 characters). For example, if Y
is the response variable with response levels 'None,' 'Mild,' and 'Severe,' the variables representing individual probabilities
Pr(Y
='None'), Pr(Y
='Mild'), and Pr(Y
='Severe') are named IP_None
, IP_Mild
, and IP_Severe
, respectively.
If you specify PREDPROBS=CUMULATIVE, the OUTPUT data set contains k additional variables that represent the cumulative probabilities, one for each response level, where k is the maximum number of response levels across all BY groups. The names of these variables have the form CP_
xxx, where xxx represents the particular response level. The naming convention is similar to that given by PREDPROBS=INDIVIDUAL. The PREDPROBS=CUMULATIVE
values are the same as those output by the PREDICT=keyword, but they are arranged in variables in each output observation
rather than in multiple output observations.
If you specify PREDPROBS=CROSSVALIDATE, the OUTPUT data set contains k additional variables representing the cross validated predicted probabilities of the k response levels, where k is the maximum number of response levels across all BY groups. The names of these variables have the form XP_
xxx, where xxx represents the particular level. The representation is the same as that given by PREDPROBS=INDIVIDUAL, except that for the
events/trials syntax there are four variables for the cross validated predicted probabilities instead of two:
XP_EVENT_R1E
is the cross validated predicted probability of an event when a current event trial is removed.
XP_NONEVENT_R1E
is the cross validated predicted probability of a nonevent when a current event trial is removed.
XP_EVENT_R1N
is the cross validated predicted probability of an event when a current nonevent trial is removed.
XP_NONEVENT_R1N
is the cross validated predicted probability of a nonevent when a current nonevent trial is removed.