Hi there,
I just started using lme4 and I have a question about obtaining p-values. I'm
trying to get p-values for the output of a linear mixed-effects model. In my
experiment I have a 2 by 2 within subjects design, fully crossing two factors,
"Gram" and "Number". This is the command I used to run the model:
>m <- lmer(RT ~ Gram*Number + (1|Subject) + (0+Gram+Number|Subject) +
(1|Item_number),data= data)
If I understand this code, I am getting coefficients for the two fixed effects
(Gram and Number) and their interaction, and I am fitting a model that has
by-subject intercepts and slopes for the two fixed effects, and a by-item
intercept for them. Following Barr et al. (2013), I thought that this code gets
rid of the correlation parameters. I don't want estimate the correlations
because I want to get the p-values using pvals.fnc (), and I read that this
function doesn't work if there are correlations in the model.
The command seems to work:
>m
Linear mixed model fit by REML
Formula: RT ~ Gram * Number + (1 | Subject) + (0 + Gram + Number | Subject)
+ (1 |Item_number)
Data: mverb[mverb$Region == "06v1", ]
AIC BIC logLik deviance REMLdev
20134 20204 -10054 20138 20108
Random effects:
Groups Name Variance Std.Dev. Corr
Item_number (Intercept) 273.508 16.5381
Subject Gramgram 0.000 0.0000
Gramungram 3717.213 60.9689 NaN
Number1 59.361 7.7046 NaN -1.000
Subject (Intercept) 14075.240 118.6391
Residual 35758.311 189.0987
Number of obs: 1502, groups: Item_number, 48; Subject, 32
Fixed effects:
Estimate Std. Error t value
(Intercept) 402.520 22.321 18.033
Gram1 -57.788 14.545 -3.973
Number1 -4.191 9.858 -0.425
Gram1:Number1 15.693 19.527 0.804
Correlation of Fixed Effects:
(Intr) Gram1 Numbr1
Gram1 -0.181
Number1 -0.034 0.104
Gram1:Nmbr1 0.000 -0.002 -0.011
However, when I try to calculate the p-values I still get an error message:
>pvals.fnc(m, withMCMC=T)$fixed
Error in pvals.fnc(m, withMCMC = T) :
MCMC sampling is not implemented in recent versions of lme4
for models with random correlation parameters
Am I making a mistake when I specify my model? Shouldn't pvals.fnc() work if I
removed the correlations?
Thanks for your help!
--Sol
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