[R-lang] Re: lmer / bootMer - calculating p-values?

[Jonathan Baron]

I think you omitted the most help page I know, which is the help page
on pvalues in the lme4 package itself:

http://finzi.psych.upenn.edu/library/lme4/html/pvalues.html

(That said, I did not check the stackoverflow page. The url is too
big.)

For me, where I used to use pvals.fnc(), I now use PBmodcomp() in the
pbkrtest package.

Jon

On 11/14/13 12:17, Moreno Coco wrote:
> Hi David,
> 
> I have also been looking into this issue very recently,
> as I am re-analyzing some time-course fixation data.
> 
> (another hot-topic that still sparkles tons of controversy,
> but nobody really discuss).
> 
> I have not found yet, a "solution" to the issue of extracting
> the p-values for the estimates of the fixed effect, as there
> really is no clear consensus on the usefulness of it, as well as,
> there is yet no technically stable solution to this issue.
> 
> there is this post:
> 
> http://stackoverflow.com/questions/18443127/r-bootstrapped-binary-mixed-model-logisti
> c-regression-using-bootmer-of-the-ne
> 
> where Ben Bolker (one of the lme4 developers) gives some
> ideas on how to perform bootstrapping, and gives a
> hint on how to extract the confidence intervals of
> parameter estimates (?confint.merMod).
> 
> He also keeps a wiki where these issues are often discussed:
> 
> http://glmm.wikidot.com/faq
> 
> An interesting discussion by Douglas Bates and other people
> about this issue can be found in:
> 
> http://rwiki.sciviews.org/doku.php?id=guides:lmer-tests
> 
> I hope this helps, and if you do find a solution, please
> share it ... I will do the same if I find one :)
> 
> Moreno
> 
> Quoting David Reitter <reitter@psu.edu> on Fri, 8 Nov 2013 15:21:37 -0500:
> 
> > Hi all,
> >
> > The latest versions of the popular 'lme4' package no longer provide  
> > an MCMC sampling function to generate p-values and confidence  
> > intervals.  You may recall that this was problematic with any bot  
> > the most basic random effects structures anyway, and lme4 authors  
> > point to random effects with low variance as the culprit.  If you  
> > use languageR to do your MCMC sampling ("pvals.fnc"), you are  
> > affected.
> >
> > One of their suggestions is to use parametric bootstrapping via "bootMer".
> >
> > I would like to run the code below by you.  Questions: shouldn't the  
> > "mySumm2" function just return the fixed effects as the statistic of  
> > interest?  (This was somewhat blindly taken from the bootMer  
> > documentation).   I then calculate the t value on the fixed effects  
> > models and read a p value from the t distribution.  I'm surprised  
> > there is no function provided - so, are there any caveats?
> >
> > Thanks,
> > David
> >
> >
> >
> > # straight from the bootMer documentation
> > mySumm2 <- function(.) {
> > c(beta=fixef(.),sigma=sigma(.),sig01=unlist(VarCorr(.)))
> > }
> >
> > # calculate prob for one-tailed t distribution from sample SAMP
> > # assuming the effect is in the predicted direction
> > dr.tfun = function (samp)  
> > {pt((mean(samp)-0)/(sd(samp)/sqrt(length(samp))), length(samp)-1,  
> > lower.tail=(mean(samp)<0))}
> >
> > # print P values [and more, without labels - fixme]
> > dr.boot.print = function (boot) { # print prob
> >     for (row in as.data.frame(boot)) {print(dr.tfun(row))}
> > }
> >
> > # bootstrap an lmer or glmer model
> > dr.bootstrap = function (model) {
> >     # bootstrap, fixed nsim
> >     bm = bootMer(model, mySumm2, nsim=20, .progress="txt")
> >     dr.boot.print(bm);
> >     return(bm)
> > }
> >
> >
> > --
> > Dr. David Reitter
> > Assistant Professor of Information Sciences and Technology
> > Penn State University
> > http://www.david-reitter.com
> >
> >
> >
> >
> >
> 
> 
> 
> -- 
> The University of Edinburgh is a charitable body, registered in
> Scotland, with registration number SC005336.
> 

-- 
Jonathan Baron, Professor of Psychology, University of Pennsylvania
Home page: http://www.sas.upenn.edu/~baron
Editor: Judgment and Decision Making (http://journal.sjdm.org)