[R] inconsistency between anova() and summary() of glmmPQL
andy_liaw at merck.com
Wed Mar 1 17:53:16 CET 2006
To quote one of my professors, it usually doesn't make sense to ask
questions like `Is variable X significant?' (Or, sort of more formally,
testing H0: beta_j = 0 vs. H1: beta_j != 0.) If `X' is the _only_ variable
you will ever consider, then the question can make sense. Otherwise, you
need more context: what other variables are you putting into the model?
The `inconsistency' you saw is because of difference in context. The test
you see in summary() adds terms in the model sequentially, so provides tests
of a sequence of nested models. OTHO, each row in the output of anova() is
comparing two models: the model with all terms (`full model') vs. the one
with all terms except the term being considered (`reduced model'). Which
one is `right' depends on which hypothesis matches your research question.
> Dear All,
> Could anyone explain me how it is possible that one factor
> in a glmmPQL model is non-significant according to the
> anova() function, whereas it turns out to be significant
> (or at least some of its levels differ significantly from
> some other levels) according to the summary() function.
> What is the truth, which results shall I believe? And, is
> there any other way of testing for the overall effect of a
> factor in glmmPQL, than anova()?
> Thanks for help,
> R-help at stat.math.ethz.ch mailing list
> PLEASE do read the posting guide!
More information about the R-help