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The LOGISTIC Procedure |

- the name of the input Data Set
- the name and label of the Response Variable if the
*single-trial*syntax is used - the number of Response Levels
- the name of the Events Variable if the
*events/trials*syntax is used - the name of the Trials Variable if the
*events/trials*syntax is used - the Number of Observations used in the analysis
- the name of the Offset Variable if the OFFSET= option is specified
- the name of the Frequency Variable if the FREQ statement is specified
- the name of the Weight Variable if the WEIGHT statement is specified
- the Sum of Weights of all the observations used in the analysis
- the Link Function
- the "Response Profile" table, which gives, for each response level,
the ordered value (an integer between
one and the number of response levels, inclusive);
the value of
the response variable if the
*single-trial*syntax is used or the values "EVENT" and "NO EVENT" if the*events/trials*syntax is used; the count or frequency; and the sum of weights if the WEIGHT statement is specified - the "Class Level Information" table, which gives the level and the design variables for each CLASS explanatory variable
- if you specify the SIMPLE option in the PROC LOGISTIC statement, the "Descriptive Statistics for Continuous Explanatory Variables" table for continuous explanatory variables, and the "Frequency Distribution of Class Variables" and the "Weight Distribution of Class Variables" tables (if the WEIGHT statement is specified). The "Descriptive Statistics for Continuous Explanatory Variables" table contains the mean, standard deviation, maximum and minimum of each continuous variable specified in the MODEL statement.
- if you use the ITPRINT option in the MODEL statement,
the "Maximum
Likelihood Iterative Phase" table, which gives the iteration number,
the step size (in the scale of 1.0, .5, .25, and so on) or the ridge value,
**-**2 log likelihood, and parameter estimates for each iteration. Also displayed are the last evaluation of the gradient vector and the last change in the**-**2 log likelihood. - if you use the SCALE= option in the MODEL statement, the Pearson and deviance goodness-of-fit statistics
- if an ordinal response model is fitted, the score test result for testing the parallel lines assumption. If LINK=CLOGLOG or LINK=PROBIT, this test is labeled "Score Test for the Parallel Slopes Assumption." The proportion odds assumption is a special case of the parallel lines assumption when LINK=LOGIT. In this case, the test is labeled "Score Test for the Proportional Odds Assumption."
- the "Model Fit Statistics" and "Testing Global Null Hypothesis: BETA=0"
tables, which give the various
criteria (
**-**2 Log L, AIC, SC) based on the likelihood for fitting a model with intercepts only and for fitting a model with intercepts and explanatory variables. If you specify the NOINT option, these statistics are calculated without considering the intercept parameters. The third column of the table gives the chi-square statistics and-values for the*p***-**2 Log L statistic and for the Score statistic. These test the joint effect of the explanatory variables included in the model. The Score criterion is always missing for the models identified by the first two columns of the table. Note also that the first two rows of the Chi-Square column are always missing, since tests cannot be performed for AIC and SC. - if you specify the RSQUARE option in the MODEL statement,
generalized
measures for the fitted model*R*^{2} - if the model contains an effect involving a CLASS variable, the
"Type III Analysis of Effects" table, which gives the Wald
Chi-square statistic, the degrees of freedom, and the
-value for each effect in the model*p* - the "Analysis of Maximum Likelihood Estimates" table, which
includes
**-**- the maximum likelihood estimate of the parameter
**-**- the estimated standard error of the parameter estimate, computed as the square root of the corresponding diagonal element of the estimated covariance matrix
**-**- the Wald chi-square statistic, computed by squaring the ratio of the parameter estimate divided by its standard error estimate
**-**- the
-value of the Wald chi-square statistic with respect to a chi-square distribution with one degree of freedom*p* **-**- if you specify the STB option in the MODEL statement,
the standardized estimate for the slope parameter,
given by
, where
is the total sample standard deviation for the*s*_{i}th explanatory variable and*i* **-**- if you specify the EXPB option in the MODEL statement, the value of (e for each slope parameter . For continuous variables, this is equivalent to the estimated odds ratio for a 1 unit change.
**-**- if you specify the PARMLABEL option in the MODEL statement, the label of the variable (if space permits). Due to constraints on the line size, the variable label may be suppressed in order to display the table in one panel. Use the SAS system option LINESIZE= to specify a larger line size to accommodate variable labels. A shorter line size can break the table into two panels allowing labels to be displayed.

- the "Odds Ratio Estimates" table, which contains the odds ratio estimates and the corresponding 95% Wald confidence intervals. For continuous explanatory variables, these odds ratios correspond to a unit increase in the risk factors.
- measures of association between predicted probabilities and
observed responses, which include a breakdown of the
number of pairs with different responses, and four
rank correlation indexes:
Somers'
*D*, Goodman-Kruskal Gamma, and Kendall's Tau-*a*, and*c* - if you use the CLPARM= option in the MODEL statement, confidence intervals for all the parameters
- if you use the CLODDS= option in the MODEL statement, confidence intervals for all the odds ratios
- if you use a FORWARD, BACKWARD, or STEPWISE selection method,
a summary of the model-building process, which gives the step
number, the explanatory variables entered or removed at each step, the
chi-square statistic, and the corresponding
-value on which the entry or removal of the variable is based (the score chi-square is used to determine entry; the Wald chi-square is used to determine removal)*p* - if you specify the FAST option in the MODEL statement,
the "Analysis of
Variables Removed by Fast Backward Elimination" table, which gives
the approximate chi-square statistic for the variable removed, the
corresponding
-value with respect to a chi-square distribution with one degree of freedom, the residual chi-square statistic for testing the joint significance of the variable and the preceding ones, the degrees of freedom, and the*p*-value of the residual chi-square with respect to a chi-square distribution with the corresponding degrees of freedom*p* - if you specify the DETAILS option in the MODEL statement,
the "Analysis of
Effects not in the Model" table, which gives
the score chi-square statistic for testing the significance of
each variable not in the model after adjusting
for the variables already in the model, and
the
-value of the chi-square statistic with respect to a chi-square distribution with one degree of freedom*p* - the classification
table if you use the CTABLE option in the MODEL statement.
For each prior event probability (labeled "Prob Event")
specified by the PEVENT= option and
each cutpoint specified in the PPROB= option,
the table gives the four entries of the
**2×2**table of observed and predicted responses and the percentages of correct classification, sensitivity, specificity, false positive, and false negative. The columns labeled "Correct" give the number of correctly classified events and nonevents. "Incorrect Event" gives the number of nonevents incorrectly classified as events. "Incorrect Nonevent" gives the number of nonevents incorrectly classified as events. - if you use the COVB option in the MODEL statement, the estimated covariance matrix of the parameter estimates
- if you use the CORRB option in the MODEL statement, the estimated correlation matrix of the parameter estimates
- the "Linear Hypothesis Testing" table, which gives the result of the Wald test for each TEST statement (if specified)
- if you use the LACKFIT option in the MODEL statement, the results of the Hosmer and Lemeshow test for the goodness of fit of the fitted model
- if you use the INFLUENCE option in the
MODEL statement, the "Regression
Diagnostics" table,
which gives, for each observation, the
case number
(which is the observation number), the values of the explanatory
variables
included in the model, the Pearson residual, the deviance residual,
the diagonal element of the hat matrix, the standardized difference in
the estimate for each parameter
(
DFBETA, where*name*is either Intercept or the name of an explanatory variable), two confidence interval displacement diagnostics (C and CBAR), the change in the Pearson chi-square statistic (DIFCHSQ), and the change in the deviance (DIFDEV)*name* - if you specified the IPLOTS option in the MODEL statement,
**-**- the index plot of Pearson residuals
**-**- the index plot of deviance residuals
**-**- the index plot of the diagonal elements of the hat matrix
**-**- index plots of the standardized differences in parameter estimates, DFBETA0 for the intercept estimate, DFBETA1 for the slope estimate of the first explanatory variable in the MODEL statement, and so on
**-**- the index plot of confidence interval displacement diagnostics C
**-**- the index plot of confidence interval displacement diagnostics CBAR
**-**- the index plot of the changes in the Pearson chi-square statistic
**-**- the index plot of the changes in the deviance

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