The SURVEYLOGISTIC Procedure

reverses the order of response categories. If both the DESCENDING and the ORDER= options are specified, PROC SURVEYLOGISTIC orders the response categories according to the ORDER= option and then reverses that order. See the section Response Level Ordering for more detail.

specifies the event category for the binary response model. PROC SURVEYLOGISTIC models the probability of the event category. The EVENT= option has no effect when there are more than two response categories. You can specify the value (formatted if a format is applied) of the event category in quotes or you can specify one of the following keywords. The default is EVENT=FIRST.

One of the most common sets of response levels is {0,1}, with 1 representing the event for which the probability is to be modeled. Consider the example where Y takes the values 1 and 0 for event and nonevent, respectively, and Exposure is the explanatory variable. To specify the value 1 as the event category, use the following MODEL statement:

   model Y(event='1') = Exposure;

specifies the sort order for the levels of the response variable.

The ORDER= option can take the following values:

By default, ORDER=INTERNAL. For ORDER=FORMATTED and ORDER=INTERNAL, the sort order is machine-dependent.

For more information about sort order, see the chapter on the SORT procedure in the Base SAS Procedures Guide and the discussion of BY-group processing in the "Grouping Data" section of SAS Programmers Guide: Essentials.

specifies the reference category for the generalized logit model and the binary response model. For the generalized logit model, each nonreference category is contrasted with the reference category. For the binary response model, specifying one response category as the reference is the same as specifying the other response category as the event category. You can specify the value (formatted if a format is applied) of the reference category in quotes or you can specify one of the following keywords. The default is REF=LAST.

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 i. 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 intervals for the parameters or odds ratios are requested.

specifies the type of likelihood ratio chi-square test. If you specify CHISQ(FIRSTORDER) or CHISQ(SECONDORDER), PROC SURVEYLOGISTIC provides a first-order or second-order (Satterthwaite) Rao-Scott likelihood ratio chi-square test, which is a design-adjusted test. If you specify CHISQ(NOADJUST), the procedure computes a chi-square test without the Rao-Scott design correction.

If you do not specify the CHISQ option, the default test that PROC SURVEYLOGISTIC uses depends on the design and model as follows:

For more information, see the section Rao-Scott Likelihood Ratio Chi-Square Test.

Note that unless you specify the DF=INFINITY option, PROC SURVEYLOGISTIC displays an F test instead of a chi-square test.

requests confidence intervals for the odds ratios. Computation of these confidence intervals is based on individual t tests or Wald tests. The degrees of freedom for a t test is described in the section Degrees of Freedom. 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 t tests or Wald tests. The degrees of freedom for a t test is described in the section Degrees of Freedom. You can specify the confidence level by using the ALPHA= option.

displays the correlation matrix of the parameter estimates.

displays the covariance matrix of the parameter estimates.

determines the denominator degrees of freedom (df) for F statistics in hypothesis testing, as well as the degrees of freedom in t tests for parameter estimates and odds ratio estimates, and for computing t distribution percentiles for confidence limits of these estimates.

You can specify type to be DESIGN, INFINITY, or PARMADJ. When you specify DF=DESIGN or DF=PARMADJ, you can optionally specify a positive value in parentheses to overwrite the default design degrees of freedom.

DF=PARMADJ is the default for the Taylor variance estimation method, and DF=DESIGN is the default for the replication variance estimation method.

For more information, see the section Degrees of Freedom.

If you specify both DF=DESIGN(value) in the MODEL statement and the DF= option in a REPWEIGHTS statement, PROC SURVEYLOGISTIC uses the value in DF=DESIGN(value) in the MODEL statement to determine the df and ignores the one in the REPWEIGHTS statement.

You can specify one of the following types:

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 i. 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 i. This is the default convergence criterion, and the default value is 1E–8. For more information, see the section Convergence Criteria.

displays the gradient vector, which is evaluated at the global null hypothesis.

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.

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 n 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 ith 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 jth parameter at iteration i. See the section Convergence Criteria for details.