Dear list, If I do not make a mistake, use of Likelihood ratio test is precluded when two models have the same number of degree of freedom. Is there a way to test which one is the best when both are close in AIC value (difference < 5) or do I have to conclude that they are "equivalent" ? Thanks Arnaud
Model selection, LRT test
4 messages · Andrew Miles, Arnaud Mosnier, Rolf Turner
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Andrew, Thanks to remind me that LRT are only for nested model ... this is not the case in my situation. Arnaud 2011/6/17 Andrew Miles <rstuff.miles at gmail.com>:
What types of models are you running? There may be two issues at play. 1. Likelihood ratio tests are only for nested models (i.e. models where the variables in one model are a subset of the variables in the other) so by definition there will always be a difference in degrees of freedom. 2. With mixed models you can only use a likelihood ratio test when the model returns a deviance score - so not for generalized linear mixed models in most cases (though I believe that you can use LRT's if they are estimated using a type of numerical integration, but not any sort of quasi-likelihood like PQL) Andrew On Fri, Jun 17, 2011 at 11:12 AM, Arnaud Mosnier <a.mosnier at gmail.com> wrote:
Dear list, If I do not make a mistake, use of Likelihood ratio test is precluded when two models have the same number of degree of freedom. Is there a way to test which one is the best when both are close in AIC value (difference < 5) or do I have to conclude that they are "equivalent" ? Thanks Arnaud
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On 18/06/11 03:12, Arnaud Mosnier wrote:
Dear list, If I do not make a mistake, use of Likelihood ratio test is precluded when two models have the same number of degree of freedom. Is there a way to test which one is the best when both are close in AIC value (difference< 5) or do I have to conclude that they are "equivalent" ?
In my understanding the LRT makes sense *only* if the two models
are *nested*; one (the ``null'' or ``restricted'' model is a sub-model
of the ``full'' or ``alternative'' model. Whence there *has* to be a
difference in the number of parameters.
I do not know if it is possible to do formal inference as to whether
one model ``improves'' over the other in settings in which the
models are not nested.
cheers,
Rolf Turner