Hi James,

As far as RLRsim, I thought there was a limitation that if random effects
(slope and intercept) were correlated, that this would not work?  Am I
wrong for thinking that?

Jacob


On Fri, Nov 22, 2013 at 5:54 AM, James Grecian
<James.Grecian@glasgow.ac.uk>wrote:

> Hi all,
>
> Have you tried Scheipl's RLRsim package?
> http://cran.r-project.org/web/packages/RLRsim/index.html
>
> I found it really useful for testing the importance of random effects. It
> will simulate a p value based on LRT for you.
>
> Best,
>
> James
>
>
> -----Original Message-----
> From: r-sig-mixed-models-bounces@r-project.org [mailto:
> r-sig-mixed-models-bounces@r-project.org] On Behalf Of Thompson,Paul
> Sent: 21 November 2013 20:22
> To: AvianResearchDivision; Philippi, Tom; r-sig-mixed-models@r-project.org
> Subject: Re: [R-sig-ME] Testing Random Effects--On the Boundary
>
> I have never heard of a rule of "dividing the p values in half". There are
> corrections like Bonferroni but these depend on the number of tests.
>
> -----Original Message-----
> From: r-sig-mixed-models-bounces@r-project.org [mailto:
> r-sig-mixed-models-bounces@r-project.org] On Behalf Of
> AvianResearchDivision
> Sent: Thursday, November 21, 2013 2:07 PM
> To: Philippi, Tom; r-sig-mixed-models@r-project.org
> Subject: Re: [R-sig-ME] Testing Random Effects--On the Boundary
>
> Hi Tom,
>
> I have read that page.  I see there are 6 options, but I am curious about
> using LRT in particular and using a corrected p value, rather than other
> options.  I see people floating around the suggestion to divide the p value
> in half, but there has to be a more exact calculation maybe?  Then again,
> maybe not because of the nature of the issue.
>
> Jacob
>
>
> On Thu, Nov 21, 2013 at 3:04 PM, Philippi, Tom <tom_philippi@nps.gov>
> wrote:
>
> > Jacob--
> > Have you read the r-sig-mixed-models FAQ and the references it points to:
> > http://glmm.wikidot.com/faq
> >
> > I don't know if you can do the parametric bootstrap tests for random
> > effects using PBmodcomp in package pbkrtest, as I only test for fixed
> > effects.
> >
> > I hope that this helps...
> > Tom
> >
> >
> > On Thu, Nov 21, 2013 at 11:47 AM, AvianResearchDivision <
> > segerfan83@gmail.com> wrote:
> >
> >> Hi all,
> >>
> >> I've read multiple times that using LRT to test the significance of
> >> random effects terms in mixed models yields conservative p-values and
> >> that one way to correct this is to divide the p value in half.  Is
> >> this a hard fast rule or is there a script for R that gives an actual
> >> corrected value?
> >>
> >> Thank you,
> >> Jacob
> >>
> >>         [[alternative HTML version deleted]]
> >>
> >> _______________________________________________
> >> R-sig-mixed-models@r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>
> >
> >
> >
> > --
> > -------------------------------------------
> > Tom Philippi, Ph.D.
> > Quantitative Ecologist & Data Therapist Inventory and Monitoring
> > Program National Park Service
> > (619) 523-4576
> > Tom_Philippi@nps.gov
> > http://science.nature.nps.gov/im/monitor
> >
>
>         [[alternative HTML version deleted]]
>
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