Displays the current HGLM model definitions (R.W. Payne, Y. Lee, J.A. Nelder & M. Noh).
Option
SAVE = pointer |
Save structure (from HGANALYSE ) to provide details of the HGLM; if omitted, information is printed for the most recently defined or fitted HGLM |
---|
No parameters
Description
HGSTATUS
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. HGSTATUS
allows you to display the current definitions of the various models.
By default the definitions are for the most recently defined or fitted HGLM, but you can use the SAVE option to supply the save structure for some other HGLM.
Options: SAVE
.
Parameters: none.
References
Lee, Y. & Nelder, J.A. (1996). Hierarchical generalized linear models (with discussion). J. R. Statist. Soc. 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
, HGDISPLAY
, HGDRANDOMMODEL
, HGFIXEDMODEL
, HGFTEST
, HGGRAPH
, HGKEEP
, HGNONLINEAR
, HGPLOT
, HGPREDICT
, HGRANDOMMODEL
, HGRTEST
, HGWALD
.
Commands for: Regression analysis.
Example
CAPTION 'HSTATUS 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 HGSTATUS