## OUTROC= Data Set

The OUTROC= data set contains data necessary for producing the ROC curve.
It has
the following variables:
- any BY variables specified
- _STEP_, the model step number. This variable is not
included if model selection is not requested.
- _PROB_, the estimated probability of an event.
These estimated probabilities serve as cutpoints
for predicting the response. Any observation with
an estimated event probability that exceeds or
equals _PROB_
is predicted to be an event; otherwise, it is predicted
to be a nonevent. Predicted probabilities
that are close to each other are grouped together, with the
maximum allowable difference between the largest and
smallest values less than
a constant that is specified by the ROCEPS=
option. The smallest
estimated probability is used to represent the group.
- _POS_, the number of correctly predicted event responses
- _NEG_, the number of correctly predicted nonevent responses
- _FALPOS_, the number of falsely predicted event responses
- _FALNEG_, the number of falsely predicted nonevent responses
- _SENSIT_, the sensitivity, which is the proportion
of event observations that were predicted to have an event
response
- _1MSPEC_, one minus specificity, which is the proportion
of nonevent observations that were predicted to have an
event response

Note that none of these statistics are affected by the bias-correction
method discussed in the "Classification Table" section.
An ROC curve is obtained by plotting _SENSIT_
against _1MSPEC_.
For more information, see the section "Receiver Operating Characteristic Curves".

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.