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
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email: lborger@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
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* 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@uzh.ch>
To: r-sig-mixed-models@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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