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 single-trial 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 single-trial 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 single-trial syntax with no BY-group processing, xxx is the left-justified 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.