HGSTATUS procedure

Displays the current HGLM model definitions (R.W. Payne, Y. Lee, J.A. Nelder & M. Noh).

Option

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, HGTOBITPOISSON, 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