HGFIXEDMODEL procedure

Defines the fixed model for a hierarchical or double hierarchical generalized linear model (R.W. Payne, Y. Lee, J.A. Nelder & M. Noh).


Options

DISTRIBUTION = string
Distribution of the data (binomial, poisson, normal, gamma); default norm

LINK = string
Link for the fixed model (identity, logarithm, logit, reciprocal, probit, complementaryloglog); default iden

DISPERSION = scalar
Value of dispersion parameter in calculation of s.e.s etc; default * for DIST=norm or gamm, and 1 for DIST=pois or bino

DLINK = string
Link for the dispersion model (logarithm, reciprocal); default loga

DTERMS = formula
Dispersion model; default * i.e. none

CONSTANT = string
How to treat the constant (estimate, omit) default esti

FACTORIAL = scalar
Limit on number of variates and/or factors in a fixed model term; default 3

WEIGHTS = variate
Prior weights; default * i.e. 1

OFFSET = variate
Offset variate; default * i.e. none

DOFFSET = variate
Offset variate for dispersion model; default * i.e. none


Parameter

TERMS = formula
Fixed model


Description

HGFIXEDMODEL is one of several procedures with the prefix HG, which provide tools for fitting the hierarchical generalized linear models defined by Lee & Nelder (1996, 2001, 2006). These models extend generalized linear models (GLMs) to include additional random terms in the linear predictor. They include generalized linear mixed models (GLMMs) as a special case, but do not constrain the additional terms to follow a Normal distribution and to have an identity link (as in the GLMM). For example, if the basic generalized linear model is a log-linear model (Poisson distribution and log link), a more appropriate assumption or the additional random terms might be a gamma distribution and a log link.

   The role of HGFIXEDMODEL is to specify the fixed model terms in the HGLM, and to define the distribution of the data (this corresponds to error distribution of a GLM). The fixed model is given by the TERMS parameter. Most of the options operate similarly to those occurring in the directives FIT and MODEL. The link function for the fixed model is defined by the LINK option, and the FACTORIAL option sets a limit on the number of variates and/or factors for a term to be included in the fixed model (default 3). The CONSTANT option indicates whether or not to include a constant term or intercept (by default this is included), and the OFFSET option allows an offset variate to be included. The DISTRIBUTION option defines the distribution of the data, the WEIGHTS option allows you to specify a variate of prior weights, and the DISPERSION option governs how the dispersion parameter is obtained.

   The HGLM methodology also caters for structured dispersion models, in which fixed terms are included in the generalized linear models that are used to estimate the dispersion parameters. Currently these GLMs must have a gamma distribution. The DTERMS option allows you to specify fixed terms for the GLM that estimates the residual dispersion parameter phi. The DLINK parameter specifies the link to use with the dispersion model, and the DOFFSET option allows you to specify an offset variate. You can also extend the GLM to become an HGLM (thus making the full model a double hierarchical generalized linear model or DHGLM), by using the HGDRANDOMMODEL procedure to add some random terms.

 

Options: DISTRIBUTION, LINK, DISPERSION, DLINK, DTERMS, CONSTANT, FACTORIAL, WEIGHTS, OFFSET, DOFFSET.

Parameter: TERMS.


Method

The information is stored in a workspace G5PL_HG (accessed using the WORKSPACE directive) for later use by HGANALYSE.


References

Lee, Y., & Nelder, J.A. (1996). Hierarchical generalized linear models (with discussion). Journal of the Royal Statistical Society, Series B, 58, 619-678.

Lee, Y., & Nelder, J.A. (2001). Hierarchical generalized linear models: a synthesis of generalised linear models, random-effect models and structured dispersions. Biometrika, 88, 987-1006.

Lee, Y. & Nelder, J.A. (2006). Double hierarchical generalized linear models (with discussion). Appl. Statist., 55, 139-185.