Diagnostics for generalised linear mixed models

Sophia Rabe-Hesketh and Anders Skrondal
Additional contact information
Anders Skrondal: Norwegian Institute of Public Health

No 5, United Kingdom Stata Users' Group Meetings 2003 from Stata Users Group

Abstract: Generalized linear mixed models are generalized linear models that include random effects varying between clusters or 'higher-level' units of hierarchically structured data. Such models can be estimated using gllamm. The prediction command gllapred can be used to obtain empirical Bayes predictions of the random effects, interpretable as higher-level residuals. Combined with approximate sampling standard deviations, these residuals can be used for identifying unusual higher-level units. However, since the distribution of these predictions is generally not known, we recommend simulating responses from the model using gllasim and comparing 'observed' and simulated residuals. We also discuss different types of level 1 residuals and influence diagnostics.

Date: 2003-03-16
References: Add references at CitEc
Citations:

Downloads: (external link)
http://fmwww.bc.edu/repec/usug2003/diag.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boc:usug03:05

Access Statistics for this paper

More papers in United Kingdom Stata Users' Group Meetings 2003 from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().