IBM SPSS COMPLEX SAMPLES 19 manual Complex Samples Logistic Regression Options

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Chapter 10

Complex Samples Logistic Regression Options

Figure 10-8

Logistic Regression Options dialog box

Estimation. This group gives you control of various criteria used in the model estimation.

„Maximum Iterations. The maximum number of iterations the algorithm will execute. Specify a non-negative integer.

„Maximum Step-Halving.At each iteration, the step size is reduced by a factor of 0.5 until the log-likelihood increases or maximum step-halving is reached. Specify a positive integer.

„Limit iterations based on change in parameter estimates. When selected, the algorithm stops after an iteration in which the absolute or relative change in the parameter estimates is less than the value specified, which must be non-negative.

„Limit iterations based on change in log-likelihood. When selected, the algorithm stops after an iteration in which the absolute or relative change in the log-likelihood function is less than the value specified, which must be non-negative.

„Check for complete separation of data points. When selected, the algorithm performs tests to ensure that the parameter estimates have unique values. Separation occurs when the procedure can produce a model that correctly classifies every case.

„Display iteration history. Displays parameter estimates and statistics at every n iterations beginning with the 0th iteration (the initial estimates). If you choose to print the iteration history, the last iteration is always printed regardless of the value of n.

User-Missing Values. All design variables, as well as the dependent variable and any covariates, must have valid data. Cases with invalid data for any of these variables are deleted from the analysis. These controls allow you to decide whether user-missing values are treated as valid among the strata, cluster, subpopulation, and factor variables.