[R-sig-ME] p-value for fixed factor in lmer
robert.espesser at lpl-aix.fr
robert.espesser at lpl-aix.fr
Thu May 16 07:46:03 CEST 2013
Hello,
To be valid, a LRT on a fixed effect must be done on lmer models
estimated with the option REML=F in the lmer() call :
modelA <- lmer(percentV ~ tempo + (1|speaker) + (1|sentence), REML=F,
data=bt.data)
For example, you may look at:
glmm.wikidot.com/
especially the FAQ
Robert Espesser
CNRS UMR 7309 - Université Aix-Marseille
5 Avenue Pasteur
13100 AIX-EN-PROVENCE
Le 16/05/2013 01:37, lborger a écrit :
> Hello,
>
> try:
>
> modelA <- lmer(percentV ~ tempo + (1|speaker) + (1|sentence),data=bt.data)
> modelB <- lmer(percentV ~ 1 + (1|speaker) + (1|sentence),data=bt.data)
> anova(modelA, modelB)
>
> HTH
>
> Cheers,
> Luca
>
>
>
> ------------------------------------------------------------------
> Luca Borger (PhD, MSc, BMus)
> Centre d'Etudes Biologiques de Chize
> CNRS (U.P.R. 1934) & INRA (USC 1339)
> 79360 Villiers-en-Bois, France
> *****
> email: lborger at cebc.cnrs.fr
> Skype: luca.borger | Tel: +33 (0)549 099613
> http://cnrs.academia.edu/LucaBorger
> http://www.researcherid.com/rid/C-6003-2008
> http://www.cebc.cnrs.fr/Fidentite/borger/borger.htm
> ------------------------------------------------------------------
> * new book chapter:
> Borger & Fryxell (2012) Quantifying individual differences in dispersal
> using the net squared displacement statistics.
> Ch. 17 In: Dispersal Ecology and Evolution. Editors: Clobert J., Baguette
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> Oxford University Press, Oxford (UK).
> -
> -----Original Message-----
> From: Volker Dellwo <volker.dellwo at uzh.ch>
> To: r-sig-mixed-models at r-project.org
> Date: Thu, 16 May 2013 00:03:08 +0200
> Subject: [R-sig-ME] p-value for fixed factor in lmer
>
>
> Dear Mixed Model users,
>
> below is an lmer function for which I calculated p-values with
> pvals.fnc. In the output I receive five p-values for the fixed factor
> 'tempo', one for each level. What I would want, however, is a p-value
> for the entire factor which I can't manage....
>
> Many thanks for any suggestions!
>
> Best wishes,
> Volker
>
>
> MODEL:
>> modelA <- lmer(percentV ~ tempo + (1|speaker) + (1|sentence),data=bt.data)
> > print(pvals.fnc(modelA))
>
> OUTPUT:
>
> $fixed
> Estimate MCMCmean HPD95lower HPD95upper pMCMC Pr(>|t|)
> (Intercept) 42.7346 42.7392 40.2256 45.1771 0.0001 0.0000
> tempo2 -0.1815 -0.1822 -1.0326 0.7087 0.6728 0.6737
> tempo3 0.7979 0.8023 -0.0953 1.6719 0.0768 0.0645
> tempo4 1.1526 1.1504 0.2812 2.0028 0.0088 0.0077
> tempo5 1.2742 1.2740 0.4183 2.1488 0.0042 0.0032
>
> $random
> Groups Name Std.Dev. MCMCmedian MCMCmean HPD95lower HPD95upper
> 1 speaker (Intercept) 3.4334 2.3338 2.3684 1.7773 3.0468
> 2 sentence (Intercept) 3.6911 2.5546 2.6462 1.6115 3.7921
> 3 Residual 3.1209 3.1974 3.2010 3.0061 3.4117
>
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