[R] Missing p-values using lmer()

[Spencer Graves]

Hi, Harold:

	  Am I correct that the tool currently preferred for estimating 
p-values for lmer is "mcmcsamp"?

Amélie:  My favorite tool for exploring the archives is 'RSiteSearch'. 
You can also get to it via www.r-project.org, but the last time I tried 
to copy a web address to paste in an email, the address I got from 
'RSiteSearch' worked but the one I got from the web page directly did not.

	  hope this helps,
	  spencer graves

Doran, Harold wrote:

> You didn't do anything wrong, lmer doesn't give them. 
And, for good reason. I've been a bit indoctrinated by
D. Bates, so let me share what I've learned.
> 
> With simple analysis of variance models with simple 
error structures, it is known that the ratio of the
variances follow and F distribution. However, with more
complex error structures, the null distribution is
unknown. Most other multilevel programs accept by
analogy that the ratio of the variances do follow an F
distribution. That is, it works well for the simple
case, therefore it probably is also true for the more
complex case.
> 
> In SAS, one can choose ddf options, such as Kenward-
Roger, which hopes that after assuming the ratio of
variances follow an F distribution, the only remaining
challenge is to properly estimate the denominator
degrees of freedom. These kinds of options do not
currently exist in lmer and after many discussions on
this list Doug Bates decided to remove the p-values for
now.
> 
> This topic has been discussed often on this list and 
you can see other discussions on the archive which may
be more insightful.
> 
> Harold
> 
> 
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Amelie LESCROEL
> Sent: Thursday, April 20, 2006 1:41 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] Missing p-values using lmer()
> 
> Hello,
> 
>  
> 
> I'm trying to perform a REML analysis using the lmer() function (lme4 package). Well, it seems to work well, except that I'm not getting any p-value (see example below). Can someone tell me what I did wrong?
> 
>  
> 
> Thanks for your help,
> 
>  
> 
> Amélie
> 
>  
> 
> 
>>library(gdata)
> 
> 
>>dive <- read.xls("C:/Documents and Settings/Amelie/My 
>>Documents/Postdoc/CE
> 
> 2005-2006/divebydive.xls", perl="C:/perl/bin/perl.exe")
> 
> 
>>library(lme4)
> 
> 
> Loading required package: Matrix
> 
> Loading required package: lattice
> 
> 
>>reml.res <- lmer(UNDS~SUCCESSMN+(1|BIRD), dive)
> 
> 
>>summary(reml.res)
> 
> 
> Linear mixed-effects model fit by REML 
> 
> Formula: UNDS ~ SUCCESSMN + (1 | BIRD) 
> 
>           Data: dive 
> 
>       AIC     BIC    logLik MLdeviance REMLdeviance
> 
>  60032.37 60053.8 -30013.19    60031.9     60026.37
> 
> Random effects:
> 
>  Groups   Name        Variance Std.Dev.
> 
>  BIRD     (Intercept)  4.4504  2.1096  
> 
>  Residual             36.4240  6.0352  
> 
> number of obs: 9324, groups: BIRD, 12
> 
>  
> 
> Fixed effects:
> 
>             Estimate Std. Error t value
> 
> (Intercept) 13.39764    0.63887 20.9707
> 
> SUCCESSMN    4.22197    4.11527  1.0259
> 
>  
> 
> Correlation of Fixed Effects:
> 
>           (Intr)
> 
> SUCCESSMN -0.276
> 
> 
>>anova(reml.res)
> 
> 
> Analysis of Variance Table
> 
>           Df Sum Sq Mean Sq
> 
> SUCCESSMN  1 38.337  38.337
> 
> 
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> 
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