Displays results from a hierarchical or double hierarchical generalized linear model analysis (R.W. Payne, Y. Lee, J.A. Nelder & M. Noh).
Options
PRINT = string tokens |
Controls printed output (model, fixedestimates, randomestimates, dispersionestimates, likelihoodstatistics, deviance, waldtests, fittedvalues); default * |
|---|---|
SEMETHOD = string token |
Method to use to calculate the se’s for the dispersion estimates (approximate, profilelikelihood); default appr |
DMETHOD = string token |
Method to use for the adjusted profile likelihood when calculating the likelihood statistics (automatic choleski, lrv); default auto |
DISPERSIONTERM = formula |
Model term for output from a dispersion analysis |
SAVE = pointer |
Save structure (from HGANALYSE) to provide details of the analysis; if omitted, output is from the most recent analysis |
No parameters
Description
HGDISPLAY 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) and described by Lee, Nelder & Pawitan (2006). The models are defined by the HGFIXEDMODEL, HGRANDOMMODEL and HGDRANDOMMODEL procedures, and fitted by the HGANALYSE procedure. HGDISPLAY allows you to display further output from the analysis.
The PRINT option specifies what output is required, with settings:
model |
details of the model that has been fitted; |
|---|---|
fixedestimates |
estimates of the fixed effects in the HGLM; |
randomestimates |
estimates of the random effects in the HGLM; |
dispersionestimates |
estimates of the parameters in the dispersion models; |
likelihoodstatistics |
likelihood statistics for assessing the models; |
deviance |
scaled deviances for assessing goodness of fit; |
waldtests |
Wald tests of the terms that can be dropped from the fixed model (see HGWALD); |
fittedvalues |
table with unit number, response variable, fitted values, residuals and leverages. |
The SEMETHOD option specifies which method to use to calculate standard errors for the estimated parameters of the dispersion models. The default, approximate, method is efficient to compute, but it may show downwards bias. However, the alternative profilelikelihood method can be very time-consuming. The DMETHOD option controls the method used to calculate the adjusted profile likelihood during the calculation of the likelihood statistics. The choleski method is fastest, while the lrv method provides a more robust alternative to use if choleski fails. The default setting, automatic, tries choleski first and then, if that fails, uses lrv instead.
By default the output is from the analysis of the mean model, but you can set the DISPERSIONTERM option to a formula defining one of the random terms to obtain information from the analysis to model its dispersion parameter.
Options: PRINT, SEMETHOD, DMETHOD, DISPERSIONTERM, SAVE.
Parameters: none.
Method
The output is mainly produced using RDISPLAY, RWALD and HGWALD.
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.
Lee, Y., Nelder, J.A. & Pawitan, Y. (2006). Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood. Chapman and Hall, Boca Raton.
See also
Procedures: HGANALYSE, HGDRANDOMMODEL, HGFIXEDMODEL, HGFTEST, HGGRAPH, HGKEEP, HGNONLINEAR, HGPLOT, HGPREDICT, HGRANDOMMODEL, HGRTEST, HGSTATUS, HGTOBITPOISSON, HGWALD.
Commands for: Regression analysis.
Example
CAPTION 'HGDISPLAY example',\
!t('Number of faults in rolls of fabric of various lengths',\
'(data from Bissell (1972) Biometrika, 59, 435-441).'),\
'Fit negative binomial: var(y) = mu + alpha * mu * mu',\
'(equivalent to Poisson-gamma HGLM with saturated random effect).';\
STYLE=meta,3(plain)
VARIATE [NVALUES=32] length,faults
READ length,faults
551 6 651 4 832 17 375 9 715 14 868 8 271 5 630 7
491 7 372 7 645 6 441 8 895 28 458 4 642 10 492 4
543 8 842 9 905 23 542 9 522 6 122 1 657 9 170 4
738 9 371 14 735 17 749 10 495 7 716 3 952 9 417 2 :
CALCULATE loglength = log(length)
& loglength = loglength - mean(loglength)
FACTOR [LEVELS=32; VALUES=1...32] saturated
HGFIXEDMODEL [DISTRIBUTION=poisson; LINK=log] loglength
HGRANDOMMODEL [DISTRIBUTION=normal; LINK=identity] saturated
HGANALYSE faults
HGDISPLAY [PRINT=deviance,fitted]
HGDISPLAY [PRINT=dispersionestimates; SEMETHOD=profile]