[R-sig-ME] p-value for fixed factor in lmer

[marKo]

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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