The SURVEYLOGISTIC Procedure

specifies the absolute function convergence criterion. Convergence requires a small change in the log-likelihood function in subsequent iterations:

where is the value of the log-likelihood function at iteration . See the section Convergence Criteria.

sets the level of significance for % confidence intervals for regression parameters or odds ratios. The value must be between 0 and 1. By default, is equal to the value of the ALPHA= option in the PROC SURVEYLOGISTIC  statement, or if the ALPHA= option is not specified. This option has no effect unless confidence limits for the parameters or odds ratios are requested.

requests confidence intervals for the odds ratios. Computation of these confidence intervals is based on individual Wald tests. The confidence coefficient can be specified with the ALPHA= option.

See the section Wald Confidence Intervals for Parameters for more information.

requests confidence intervals for the parameters. Computation of these confidence intervals is based on the individual Wald tests. The confidence coefficient can be specified with the ALPHA= option.

See the section Wald Confidence Intervals for Parameters for more information.

displays the correlation matrix of the parameter estimates.

displays the covariance matrix of the parameter estimates.

displays the exponentiated values (e) of the parameter estimates in the "Analysis of Maximum Likelihood Estimates" table for the logit model. These exponentiated values are the estimated odds ratios for the parameters corresponding to the continuous explanatory variables.

specifies the relative function convergence criterion. Convergence requires a small relative change in the log-likelihood function in subsequent iterations:

where is the value of the log likelihood at iteration . See the section Convergence Criteria for details.

specifies the relative gradient convergence criterion. Convergence requires that the normalized prediction function reduction is small:

where is the value of the log-likelihood function, is the gradient vector, and the (expected) information matrix. All of these functions are evaluated at iteration . This is the default convergence criterion, and the default value is 1E8. See the section Convergence Criteria for details.

displays the iteration history of the maximum-likelihood model fitting. The ITPRINT option also displays the last evaluation of the gradient vector and the final change in the .

specifies the link function that links the response probabilities to the linear predictors. You can specify one of the following keywords. The default is LINK=LOGIT.

CLOGLOG

specifies the complementary log-log function. PROC SURVEYLOGISTIC  fits the binary complementary log-log model for binary response and fits the cumulative complementary log-log model when there are more than two response categories. Aliases: CCLOGLOG, CCLL, CUMCLOGLOG.

GLOGIT

specifies the generalized logit function. PROC SURVEYLOGISTIC  fits the generalized logit model where each nonreference category is contrasted with the reference category. You can use the response variable option REF= to specify the reference category.

LOGIT

specifies the cumulative logit function. PROC SURVEYLOGISTIC  fits the binary logit model when there are two response categories and fits the cumulative logit model when there are more than two response categories. Aliases: CLOGIT, CUMLOGIT.

PROBIT

specifies the inverse standard normal distribution function. PROC SURVEYLOGISTIC  fits the binary probit model when there are two response categories and fits the cumulative probit model when there are more than two response categories. Aliases: NORMIT, CPROBIT, CUMPROBIT.

See the section Link Functions and the Corresponding Distributions for details.

specifies the maximum number of iterations to perform. By default, MAXITER=25. If convergence is not attained in iterations, the displayed output created by the procedure contains results that are based on the last maximum likelihood iteration.

disables the checking process to determine whether maximum likelihood estimates of the regression parameters exist. If you are sure that the estimates are finite, this option can reduce the execution time when the estimation takes more than eight iterations. For more information, see the section Existence of Maximum Likelihood Estimates.

suppresses the "Class Level Information" table, which shows how the design matrix columns for the CLASS variables are coded.

suppresses the intercept for the binary response model or the first intercept for the ordinal response model.

names the offset variable. The regression coefficient for this variable is fixed at 1.

displays the labels of the parameters in the "Analysis of Maximum Likelihood Estimates" table.

specifies the technique used to improve the log-likelihood function when its value in the current iteration is less than that in the previous iteration. If you specify the RIDGING=ABSOLUTE option, the diagonal elements of the negative (expected) Hessian are inflated by adding the ridge value. If you specify the RIDGING=RELATIVE option, the diagonal elements are inflated by a factor of 1 plus the ridge value. If you specify the RIDGING=NONE option, the crude line search method of taking half a step is used instead of ridging. By default, RIDGING=RELATIVE.

requests a generalized measure for the fitted model.

For more information, see the section Generalized Coefficient of Determination.

specifies the tolerance for testing the singularity of the Hessian matrix (Newton-Raphson algorithm) or the expected value of the Hessian matrix (Fisher scoring algorithm). The Hessian matrix is the matrix of second partial derivatives of the log likelihood. The test requires that a pivot for sweeping this matrix be at least this value times a norm of the matrix. Values of the SINGULAR= option must be numeric. By default, SINGULAR=.

displays the standardized estimates for the parameters for the continuous explanatory variables in the "Analysis of Maximum Likelihood Estimates" table. The standardized estimate of is given by , where is the total sample standard deviation for the th explanatory variable and

For the intercept parameters and parameters associated with a CLASS variable, the standardized estimates are set to missing.

specifies the optimization technique for estimating the regression parameters. NEWTON (or NR) is the Newton-Raphson algorithm and FISHER (or FS) is the Fisher scoring algorithm. Both techniques yield the same estimates, but the estimated covariance matrices are slightly different except for the case where the LOGIT link is specified for binary response data. The default is TECHNIQUE=FISHER. If the LINK=GLOGIT option is specified, then Newton-Raphson is the default and only available method. See the section Iterative Algorithms for Model Fitting for details.

specifies an adjustment to the variance estimation for the regression coefficients.

By default, PROC SURVEYLOGISTIC  uses the degrees of freedom adjustment VADJUST=DF.

If you do not want to use any variance adjustment, you can specify the VADJUST=NONE option. You can specify the VADJUST=MOREL option for the variance adjustment proposed by Morel (1989).

You can specify the following Morel-options within parentheses after the VADJUST=MOREL option:

specifies the relative parameter convergence criterion. Convergence requires a small relative parameter change in subsequent iterations:

where

and is the estimate of the th parameter at iteration . See the section Convergence Criteria for details.