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I have run the following model using lmerTest and using lme4:

model2 = lmer(log(RT)~Group*A*B*C+(1|item)+(1+A+B+C|subject),data=dt)

Using lmerTest I get the following error when typing the summary() command:

> summary(model1)
Error in `colnames<-`(`*tmp*`, value = c("Estimate", "Std. Error", "df",  : 
  length of 'dimnames' [2] not equal to array extent

I saw this has already been an issue for other users and that one user was able to bypass the issue running lsmeans(). When I tried lsmeans, I got the error:

Error in asMethod(object) : not a positive definite matrix.

I did not see any NAs when looking into the covariance matrix. Note that I am able to run this model if I simply inverse the contrasts in the Group factor. I have difficulties understanding why this is the case.

When I run the same model using lme4 and not lmerTest, I am able to get all the outputs of summary() but no p-values (as expected). pvals.fnc is discontinued in lme4 and I have not found an alternative yet. Plus it would be nice to have the p-values estimated in the same way for model2 as for the other models for which I was successfully able to use lmerTest.

Does anyone know what I should do at this point? Any help would be much appreciated!

Shevliaskovic
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stephanie
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1 Answers1

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If A or B or C are factors then you might get errors - such models are not yet supported by the lmerTest package (we will put the warning message together with the restrictions for such models in the help page)

alku
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  • Thank you very much for your answer. However, I have been able to run very similar models without any problems. Even the model I sent works fine if I just inverse the contrasts of the between-subject factor Group. – stephanie Jul 15 '14 at 17:11