HGPLOT procedure
Produces model-checking plots for a hierarchical or double hierarchical generalized linear model analysis (R.W. Payne, Y. Lee, J.A. Nelder & M. Noh).
Options
Parameters
Description
HGPLOT is one of several procedures with the prefix HG, which provide tools for fitting the hierarchical and double hierarchical generalized linear models (HGLMs and DHGLMs) defined by Lee & Nelder (1996, 2001, 2006). The models are defined by the HGFIXEDMODEL, HGRANDOMMODEL and HGDRANDOMMODEL procedures, and fitted by the HGANALYSE procedure. HGPLOT displays plots of residuals to help with model checking.
Six types of plot are available. They are selected using the METHOD parameter
with settings:
Up to four can be examined in any call of the procedure. The PEN parameter can be used to specify the graphics pen or pens to use for each plot. The TITLE option can supply an overall title. If this is not set, the identifier of the y-variate is used.
The MODELTYPE option indicates the type of model for which the plots are required. The default setting mean requests plots from the mean model, and the alternative setting dispersion obtains plots from the dispersion model. The RANDOMTERM option specifies the random term whose residuals are to be plotted; if this is omitted the plot is for the residual term (phi). If a DHGLM has been fitted, you can plot residuals from the HGLM that is being used as a dispersion model by setting the DHGRANDOMTERM parameter to the random term concerned. The type of residual to plot is specified by the RMETHOD option; by default these are deviance residuals.
By default, high-resolution graphics are used. Line-printer graphics can be used by setting option GRAPHICS=lineprinter.
Options: MODELTYPE, RANDOMTERM, DHGRANDOMTERM, RMETHOD, INDEX, GRAPHICS, TITLE, SAVE.
Parameters: METHOD, PEN.
Method
HGPLOT calls procedure DRESIDUALS to do the plotting.
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.