Pierre Walthery wrote:
I am fitting a 2 levels random intercept model. The model is a
multinomial logit. In order to fit it, I am using gllamm, with no
random effects specified. I then get the normal gllamm output with an
estimate of level 2 variance.
My question is: how can I test whether this level 2 variance is
significant - say by comparison with a one level mlogit model? I know
about the possibility to use a likelihood ratio test to compare two
nested models, but this would not allow me to test specifically for
level 2 variance, or is there anything I am missing here?
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I'm not very familiar with multinomial logistic modeling in particular, but
in general I thought that a likelihood ratio test is indeed how you would
test the hypothesis that the level-2 variance component is greater than zero
with -gllamm-.
The general form is
gllamm . . . , i(id)
estimates store RandomEffectsIncluded
mlogit . . . // use identical fixed effects as with -gllamm-
lrtest RandomEffectsIncluded ., force
There is some considerations involved in the assignment of the probability
associated with the likelihood ratio chi-square test statistic in these
cases. This is in order to take into account that the null hypothesis is on
the boundary of the parameter space. It is explained in -help j_chibar-.
Joseph Coveney
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