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Chapter 10
Complex Samples Logistic Regression Options
Figure 10-8
Logistic Regression Options d ialog box
Estimation. This group gives yo u 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 0
th
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.