[R-sig-ME] how to know if random factors are significant?

[John Maindonald]

There was a related question from Mariana Martinez a day or two ago.   
Before removing a random term that background knowledge or past  
experience with similar data suggests is likely, check what difference  
it makes to the p-values for the fixed  effects that are of interest.   
If it makes a substantial difference, caution demands that it be left  
it in.

To pretty much repeat my earlier comment:
If you omit the component then you have to contemplate the alternatives:
1) the component really was present but undetectable
2) the component was not present, or so small that it could be  
ignored, and the inference from the model that omits it is valid.

If (1) has a modest probability, and it matters whether you go with  
(1) or (2), going with (2) leads to a very insecure inference. The p- 
value that comes out of the analysis is unreasonably optimistic; it is  
wrong and misleading.

If you do anyway want a Bayesian credible interval, which you can  
treat pretty much as a confidence interval, for the random component,  
check Douglas Bates' message of a few hours ago, the first of two  
messages with the subject "lme4::mcmcsamp + coda::HPDinterval", re the  
use of the function HPDInterval().

John Maindonald             email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473    fax  : +61 2(6125)5549
Centre for Mathematics & Its Applications, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.


On 2 Apr 2008, at 4:02 AM, Leonel Arturo Lopez Toledo wrote:

> Dear all:
> I’m new to mixed models and I’m trying to understand the output from  
> “lme” in the nlme
> package. I hope my question is not too basic for that list-mail.  
> Really sorry if that
> is the case.
> Especially I have problems to interpret the random effect output. I  
> have only one
> random factor which is “Site”. I know the “Variance and Stdev”  
> indicate variation by
> the random factor, but are they indicating any significance? Is  
> there any way to
> obtain a p-value for the random effects? And in case is not  
> significant, how can I
> remove it from the model? With “update (model,~.-)”?
>
> The variance in first case (see below) is very low and in the second  
> example is more
> considerable, but should I consider in the model or do I remove it?
>
> Thank you very much for your help in advance.
>
> EXAMPLE 1
> Linear mixed-effects model fit by maximum likelihood
> Data: NULL
>       AIC      BIC    logLik
>  277.8272 287.3283 -132.9136
>
> Random effects:
> Formula: ~1 | Sitio
>         (Intercept) Residual
> StdDev: 0.0005098433 9.709515
>
> EXAMPLE 2
> Generalized linear mixed model fit using Laplace
> Formula: y ~Canopy*Area + (1 | Sitio)
>   Data: tod
> Family: binomial(logit link)
>   AIC   BIC logLik deviance
> 50.93 54.49 -21.46    42.93
>
> Random effects:
> Groups Name        Variance Std.Dev.
> Sitio  (Intercept) 0.25738  0.50733
> number of obs: 18, groups: Sitio, 6
>
>
> Leonel Lopez
> Centro de Investigaciones en Ecosistemas-UNAM
> MEXICO
>
>
>
>
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