p-value for fixed factor in lmer
Hi. I've read some stuff on problems estimating p-values in mixed models (Bates wrote something about it, hence it was not introduces in lme4; you get only t-values but not p). As i understand it, you could resort to 3 practice. 1. take the t-value as z-value (that's the approach that Baayen advocate under most circumstances, if i recall it correctly). 2. use MCMC sampling 3. make a model comparison (like Luca suggested) I'd go with the model comparison (I think it is the most robust way) and gives you the estimates of significance for the factos as a whole (not for the dummy coding in the background). Hope it helps. Regards, Marko
On 16.05.2013 01:37, lborger wrote:
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 M., Benton T., Bullock J. 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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