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The LOGISTIC Procedure |

**OUTPUT****<****OUT=**SAS-data-set**>****<**options**>****;**

If you use the

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.

**OUT=***SAS-data-set*-
names the output data set.
If you omit the OUT= option, the output data set is created
and given a default name using the DATA
convention.*n*

The following sections explain options in the OUTPUT statement, divided into statistic options for any type of response variable, statistic options only for binary response, and other options. The statistic options specify the statistics to be included in the output data set and name the new variables that contain the statistics.

**LOWER=***name***L=***name*-
specifies the lower confidence limit for the probability of an event response
if
*events/trials*syntax is specified, or the lower confidence limit for the probability that the response is less than or equal to the value of _LEVEL_ if*single-trial*syntax is specified. See the ALPHA= option. **PREDICTED=***name***PRED=***name***PROB=***name***P=***name*-
specifies the predicted probability of an event response if
*events/trials*syntax is specified, or the predicted probability that the response variable is less than or equal to the value of _LEVEL_ if*single-trial*syntax is specified (in other words, Pr**(**Y _LEVEL_**)**, where Y is the response variable). **PREDPROBS=(***keywords*)-
requests
individual, cumulative, or cross-validated predicted probabilities.
Descriptions of the
*keywords*are as follows.- INDIVIDUAL | I
- requests the predicted probability of each
response level.
For a response variable Y with three levels, 1, 2, and 3, the
individual probabilities are Pr(Y=1),
Pr(Y=2), and Pr(Y=3).
- CUMULATIVE | C
- requests the cumulative predicted probability of
each response level.
For a response variable Y with three response levels, 1,2, and 3, the
cumulative probabilities are Pr(Y1), Pr(Y2),
and Pr(Y3). The cumulative probability for the last
response level always has the constant value of 1.
- CROSSVALIDATE | XVALIDATE | X
- requests the cross-validated individual predicted probability of each response level. These probabilities are derived from the leave-one-out principle; that is, dropping the data of one subject and reestimating the parameter estimates. PROC LOGISTIC uses a less expensive one-step approximation to compute the parameter estimates. Note that, for ordinal models, the cross validated probabilities are not computed and are set to missing.

See the end of this section for further details regarding the PREDPROBS= option. **STDXBETA=***name*-
specifies the standard error estimate of
XBETA
**UPPER=***name***U=***name*-
specifies the upper confidence limit for the probability of an event response
if
*events/trials model*is specified, or the upper confidence limit for the probability that the response is less than or equal to the value of _LEVEL_ if*single-trial*syntax is specified. See the ALPHA=option. **XBETA=***name*-
specifies the estimate of the linear predictor
, where
is the corresponding ordered value of _LEVEL_.*i*

**C=***name*-
specifies the confidence interval displacement diagnostic that measures the
influence of individual observations on the regression
estimates.
**CBAR=***name*-
specifies the another confidence interval displacement diagnostic, which measures the
overall change in the global regression estimates due to deleting
an individual observation.
**DFBETAS= _ALL_****DFBETAS=***var-list*-
specifies the standardized differences in the regression estimates for assessing
the effects of individual observations on the estimated regression
parameters in the fitted model. You can specify a list of
up to
variable names, where*s*+1is the number of explanatory variables in the MODEL statement, or you can specify just the keyword _ALL_. In the former specification, the first variable contains the standardized differences in the intercept estimate, the second variable contains the standardized differences in the parameter estimate for the first explanatory variable in the MODEL statement, and so on. In the latter specification, the DFBETAS statistics are named DFBETA_*s*, where*xxx*is the name of the regression parameter. For example, if the model contains two variables X1 and X2, the specification DFBETAS=_ALL_ produces three DFBETAS statistics named DFBETA_Intercept, DFBETA_X1, and DFBETA_X2. If an explanatory variable is not included in the final model, the corresponding output variable named in DFBETAS=*xxx**var-list*contains missing values. **DIFCHISQ=***name*-
specifies the change in the chi-square goodness-of-fit statistic attributable to
deleting the individual observation.
**DIFDEV=***name*-
specifies the change in the deviance attributable to deleting the individual
observation.
**H=***name*-
specifies the diagonal element of the hat matrix for detecting extreme points in the
design space.
**RESCHI=***name*-
specifies the Pearson (Chi) residual for identifying observations
that are poorly accounted for by the model.
**RESDEV=***name*-
specifies the deviance residual for identifying poorly fitted
observations.

**ALPHA=***value*-
sets the confidence level used for the confidence limits for the
appropriate response probabilities. The quantity
*value*must be between 0 and 1. By default, ALPHA=0.05, which results in the calculation of a 95% confidence interval.

When you specify the PREDPROBS= option, two automatic variables _FROM_ and _INTO_ are included for the

If you specify PREDPROBS=INDIVIDUAL, the OUTPUT data set contains

- If you specify
*events/trials*syntax,*xxx*is either `Event' or `Nonevent'. Thus, the variable containing 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 containing 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 may 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'), P(Y='Mild'), and P(Y='Severe') are named IP_None, IP_Mild, and IP_Severe, respectively.

If you specify PREDPROBS=CUMULATIVE, the OUTPUT data set contains

If you specify PREDPROBS=CROSSVALIDATE, the OUTPUT data set contains

- XP_EVENT_R1E
- is the cross validated predicated probability of
an event when a current event trial is removed.
- XP_NONEVENT_R1E
- is the cross validated predicated probability
of
a nonevent when a current event trial is removed.
- XP_EVENT_R1N
- is the cross validated predicated probability of
an event when a current nonevent trial is removed.
- XP_NONEVENT_R1N
- is the cross validated predicated probability of a nonevent when a current nonevent trial is removed.

The cross-validated predicted probabilities are precisely those used in the CTABLE option. Refer to the "Predicted Probability of an Event for Classification" section for details of the computation.

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