Re: [R] Effect size in mixed models

From: Spencer Graves <spencer.graves_at_pdf.com>
Date: Tue 20 Jun 2006 - 12:52:24 EST

          You have asked a great question: It would indeed be useful to compare the relative magnitude of fixed and random effects, e.g. to prioritize efforts to better understand and possibly manage processes being studied. I will offer some thoughts on this, and I hope if there are errors in my logic or if someone else has a better idea, we will both benefit from their comments.

          The ideal might be an estimate of something like a mean square for a particular effect to compare with an estimated variance component. Such mean squares were a mandatory component of any analysis of variance table prior to the (a) popularization of generalized linear models and (b) availability of software that made it feasible to compute maximum likelihood estimates routinely for unbalanced, mixed-effects models. However, anova(lme(...)) such mean squares are for most purposes unnecessary cluster in a modern anova table.

          To estimate a mean square for a fixed effect, consider the following log(likelihood) for a mixed-effects model:

          lglk = (-0.5)*(n*log(2*pi*var.e)-log(det(W)) + t(y-X%*%b)%*%W%*%(y-X%*%b)/var.e),

where n = the number of observations,

       b = the fixed-effect parameter variance,

and the covariance matrix of the residuals, after integrating out the random effects is var.e*solve(W). In this formulation, the matrix "W" is a function of the variance components. Since they are not needed to compute the desired mean squares, they are suppressed in the notation here.

          Then, the maximum likelihood estimate of

          var.e = SSR/n,

where SSR = t(y-X%*%b)%*%W%*%(y-X%*%b).

          Then

          mle.lglk = (-0.5)*(n*(log(2*pi*SSR/n)-1)-log(det(W))).

          Now let

          SSR0 = this generalized sum of squares of residuals (SSR) without effect "1",

and

          SSR1 = this generalized SSR with this effect "1".

          If I've done my math correctly, then

	  D = deviance = 2*log(likelihood ratio)
	    = (n*log(SSR0/SSR1)+log(det(W1)/det(W0)))

	  For roughly half a century, a major part of "the analysis of 
variance" was the Pythagorean idea that the sum of squares under H0 was the sum of squares under H1 plus the sum of squares for effect "1":

          SSR0 = SS1 + SSR1.           Whence,

          exp((D/n)-log(det(W1)/det(W0))) = 1+SS1/SSR1.

Thus,

          SS1 = SSR1*(exp((D/n)-log(det(W1)/det(W0)))-1).

          If the difference between deg(W1) and det(W0) can be ignored, we get:

          SS1 = SSR1*(exp((D/n)-1).

          Now compute MS1 = SS1/df1, and compare with the variance components.

          If there is a flaw in this logic, I hope someone will disabuse me of it.

          If this seems too terse or convoluted to follow, please provide a simple, self-contained example, as suggested in the posting guide! "www.R-project.org/posting-guide.html". You asked a theoretical question, you got a theoretical answer. If you want a concrete answer, it might help to pose a more concrete question.

	  Hope this helps.
	  Spencer Graves	

Bruno L. Giordano wrote:
> Hello,
> Is there a way to compare the relative relevance of fixed and random effects
> in mixed models? I have in mind measures of effect size in ANOVAs, and would
> like to obtain similar information with mixed models.
>
> Are there information criteria that allow to compare the relevance of each
> of the effects in a mixed model to the overall fit?
>
> Thank you,
> Bruno
>
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R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Jun 20 12:56:51 2006

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