Hierarchical Generalized Linear Model Options

Use this to set options and select the output to be generated by Hierarchical Generalized Linear Model analysis.

Display

This specifies which items of output are to be produced by the analysis.

Model	Details of the model that has been fitted
Deviance	Estimates of the deviances
Monitoring	Monitoring information at each iteration
Fitted values	Table with unit number, response variable, fitted values, residuals and leverages
Estimates (fixed model)	Estimates of the parameters in the fixed model
Estimates (random model)	Estimates of the parameters in the random model
Estimates (dispersion)	Estimates of the parameters in the dispersion models (if any)
Likelihood statistics	Likelihood statistics for assessing the models
Wald tests (fixed model)	Wald statistics for the terms in the fixed model
Estimates of censored observations	When censoring is selected for a Poisson-log link model, this prints the estimates of censored observations

Dispersion parameter

Controls whether the dispersion parameter for the variance of the response is estimated from the residual mean square of the fitted model, or fixed at a given value. The dispersion parameter (fixed or estimated) is used when calculating standard errors and standardized residuals. In models with the binomial or Poisson distributions, the dispersion should be fixed at 1 unless a heterogeneity parameter is to be estimated.

Estimate constant term

Specifies whether to include a constant in the HGLM fixed model. If the constant is not included, the intercept term will be zero.

Maximum number of iterations

GLM	maximum number of iterations of the hierarchical generalized linear algorithm
Mean dispersion model	maximum number of iterations in the fitting of the mean and dispersion models
Censored E-M	maximum number of iterations in the fitting censored observations with the Tobit expectation-maximization algorithm

Convergence tolerances

HGLM	maximum change in hierarchical generalized linear model parameters between iterations
Aitken extrapolation	maximum size of ratio of the original to the new estimates allowed in Aitken extrapolation
Censored E-M	maximum change in estimated censored observations with the Tobit expectation-maximization algorithm

Order of Laplace approximation in mean model

This can be used to set the order of the Laplace approximation to use in the estimation of the mean model.

Order of Laplace approximation in dispersion model

This can be used to set the order of the Laplace approximation involved in the estimation of the dispersion components of the model. This is appropriate for generalized linear mixed models with the binomial or Poisson distributions, where use of Laplace order 0 can lead to serious downwards bias. Caution must be used when setting an order of 2 as the analysis can take a long time to run.

Offset

A fixed model in an HGLM can be modified to take account of a fixed contribution to the linear effects for each unit, supplied in a variate referred to as the offset.

Weights

This lets you specify a variate of prior for the weights for the fixed model.

Factorial limit on fixed model terms

For the fixed model you can control the factorial limit on model terms to be generated when you use model-formula operators like *. The default is to include all interactions, up to those involving nine variates or factors.

Action buttons

Run	Save options and close dialog.
Cancel	Close dialog without saving the options.
Defaults	Reset the options to their default settings.

Action Icons

	Clear	Clear all fields and list boxes.
	Help	Open the Help topic for this dialog.