Estimation (generalized linear mixed models)

The model building algorithm uses a doubly iterative process that consists of an inner loop and an outer loop. The following settings apply to the inner loop.

Sorting Order

These controls determine the order of the categories for the target and factors (categorical inputs) for purposes of determining the "last" category. The target sort order setting is ignored if the target is not categorical or if a custom reference category is specified on the Target (generalized linear mixed models) settings.

Parameter Convergence.

Convergence is assumed if the maximum absolute change or maximum relative change in the parameter estimates is less than the value specified, which must be non-negative. The criterion is not used if the value specified equals 0.

Log-likelihood Convergence.

Convergence is assumed if the absolute change or relative change in the log-likelihood function is less than the value specified, which must be non-negative. The criterion is not used if the value specified equals 0.

Hessian Convergence.

For the Absolute specification, convergence is assumed if a statistic based on the Hessian is less than the value specified. For the Relative specification, convergence is assumed if the statistic is less than the product of the value specified and the absolute value of the log-likelihood. The criterion is not used if the value specified equals 0.

Maximum Fisher scoring steps.

Specify a non-negative integer. A value of 0 specifies the Newton-Raphson method. Values greater than 0 specify to use the Fisher scoring algorithm up to iteration number n, where n is the specified integer, and Newton-Raphson thereafter.

Singularity tolerance.

This value is used as the tolerance in checking singularity. Specify a positive value.

Stopping Rules

You can specify the maximum number of iterations the algorithm will execute. The algorithm uses a doubly iterative process that consists of an inner loop and an outer loop. The value that is specified for the maximum number of iterations applies to both loops. Specify a non-negative integer. The default is 100.

Post-Estimation Settings

These settings determine how some of the model output is computed for viewing.

Confidence level (%)

This is the level of confidence used to compute interval estimates of the model coefficients. Specify a value greater than 0 and less than 100. The default is 95.

Degrees of freedom

This specifies how degrees of freedom are computed for significance tests. Choose Residual method if your sample size is sufficiently large, or the data are balanced, or the model uses a simpler covariance type (for example, scaled identity or diagonal). This is the default setting. Choose Satterthwaite approximation if your sample size is small, or the data are unbalanced, or the model uses a complicated covariance type (for example, unstructured). Choose Kenward-Roger approximation if your sample size is small and you have a Restricted Maximum Likelihood (REML) model.

Tests of fixed effects and coefficients

This is the method for computing the parameter estimates covariance matrix. Choose the robust estimate if you are concerned that the model assumptions are violated.