Colnames error after running Summary() in mixed model - r

Question

R version 3.1.0 (2014-04-10)
lmer package version 1.1-6
lmerTest package version 2.0-6
I am currently working with lmer and lmerTest for my analysis.
Every time I add an effect to the random structure, I get the following error when running summary():
#Fitting a mixed model:
TRT5ToVerb.lmer3 = lmer(TRT5ToVerb ~ Group + Condition + (1+Condition|Participant) + (1|Trial), data=AllData, REML=FALSE, na.action=na.omit)
summary(TRT5ToVerb.lmer3)
Error in `colnames<-`(`*tmp*`, value = c("Estimate", "Std. Error", "df", : length of 'dimnames' [2] not equal to array extent
If I leave the structure like this:
TRT5ToVerb.lmer2 = lmer(TRT5ToVerb ~ Group + Condition + (1|Participant) + (1|Trial), data=AllData, REML=FALSE, na.action=na.omit)
there is no error run summary(TRT5ToVerb.lmer2), returning AIC, BIC, logLik deviance, estimates of the random effects, estimates of the fixed effects and their corresponding p-values, etc., etc.
So, apparently something happens when I run lmerTest, despite the fact that the object TRT5ToVerb.lmer3 is there. The only difference between both is the random structure: (1+Condition|Participant) vs. (1|Participant)
Some characteristics of my data:
Both Condition and Group are categorical variables: Condition
comprises 3 levels, and Group 2
The dependent variable (TRT5ToVerb) is continuous: it corresponds to
reading time in terms of ms
This a repeated measures experiment, with 48 observations per
participant (participants=28)
I read this threat, but I cannot see a clear solution. Will it be that I have to transform my dataframe to long format?
And if so, then how do I work with that in lmer?
I hope it is not that.
Thanks!
Disclaimer: I am neither an expert in R, nor in statistics, so please, have some patience.

Answer 1

(Should be a comment, but too long/code formatting etc.)
This fake example seems to work fine with lmerTest 2.0-6 and a development version of lme4 (1.1-8; but I wouldn't expect there to be any relevant differences from 1.1-6 for this example ...)
AllData <- expand.grid(Condition=factor(1:3),Group=factor(1:2),
Participant=1:28,Trial=1:8)
form <- TRT5ToVerb ~ Group + Condition + (1+Condition|Participant) + (1|Trial)
library(lme4)
set.seed(101)
AllData$TRT5ToVerb <- simulate(form[-2],
newdata=AllData,
family=gaussian,
newparam=list(theta=rep(1,7),sigma=1,beta=rep(0,4)))[[1]]
library(lmerTest)
lmer3 <- lmer(form,data=AllData,REML=FALSE)
summary(lmer3)
Produces:
Linear mixed model fit by maximum likelihood ['merModLmerTest']
Formula: TRT5ToVerb ~ Group + Condition + (1 + Condition | Participant) +
(1 | Trial)
Data: AllData
AIC BIC logLik deviance df.resid
4073.6 4136.0 -2024.8 4049.6 1332
Scaled residuals:
Min 1Q Median 3Q Max
-2.97773 -0.65923 0.02319 0.66454 2.98854
Random effects:
Groups Name Variance Std.Dev. Corr
Participant (Intercept) 0.8546 0.9245
Condition2 1.3596 1.1660 0.58
Condition3 3.3558 1.8319 0.44 0.82
Trial (Intercept) 0.9978 0.9989
Residual 0.9662 0.9829
Number of obs: 1344, groups: Participant, 28; Trial, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.49867 0.39764 12.40000 1.254 0.233
Group2 0.03002 0.05362 1252.90000 0.560 0.576
Condition2 -0.03777 0.22994 28.00000 -0.164 0.871
Condition3 -0.27796 0.35237 28.00000 -0.789 0.437
Correlation of Fixed Effects:
(Intr) Group2 Cndtn2
Group2 -0.067
Condition2 0.220 0.000
Condition3 0.172 0.000 0.794