[R-sig-ME] Best practice for co/variance component testing in LMM

[Ben Pelzer]

Hi Maarten,

It sounds as if paragraph 6.2.1 in Snijders & Bosker 2nd edition of 
"Multilevel Analysis" gives an answer to your question. Regards,

Ben.

On 18-6-2018 10:09, Maarten Jung wrote:
> What is the best way to test if multiple co/variance components in a
> linear mixed model improve the model fit?
> When testing the null hypothesis that variance components are zero the
> alternative hypothesis is one-sided, the sampling distribution of the
> anova()-LR-statistic is not welI approximated by a chi-square
> distribution and the LRT is conservative in this "boundary case". I
> know there is RLRsim but I couldn't figure out how to test for
> multiple variance components with exactRLRT/exactLRT. Besides that
> RLRsim cannot be used to test the null hypothesis that a covariance is
> equal to zero.
> I came up with the idea to use LRT based on chi-bar-square
> distributions, which have known weights following a binomial
> distribution [1], for testing the (uncorrelated) variance components.
> When testing the covariance the parameter value in the null hypothesis
> is no longer on the edge of the parameter space and I think the LRT
> via anova() should be, at least asymptotically, correct.
> Are there better ways and/or other R packages for this purpose,
> especially for merMod objects?
>
> Cheers,
> Maarten
>
> [1] http://www.jstor.org/stable/27643833
>
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