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One relatively straight-forward way could be to use likelihood-ratio tests via anova as described in the lme4 FAQ. We start with a full model in which the variances are unconstrained (i.e., two diffe …

Thanks a lot for reporting this. It is indeed a mistake in the documentation. This and all the following contrasts were wrong. I guess when I wrote the code the ordering of the factor was different or …

The default behavior for lme4::lmer is to use restricted maximum likelihood estimation. If you switch to 'pure' maximum likelihood estimation, the result of 0 variance replicates: l2 <- lmer(y ~ (1|s …

Models m2 and m3 can only be estimated if you have repetitions on the level of the participant-item combination. For m3 for the full factorial design. Otherwise this random effect is confounded with t …

I agree with everything said in amoeba's answer which provides a great summary of the current discussion on this issue. I will try to add a few additional points and otherwise refer to the handout of …

The full quote from our chapter also provides an answer to your second question (i.e., the why): A common contrast scheme, which is the default in R, is called treatment contrasts (i.e., contr.treatment …

This can be solved by using the ingenious combination of afex with lsmeans (and also multcomp if one desires so, but this is usually not necessary). Furthermore, thanks to afex functionality to aggreg …

R uses treatment-contrasts per default with the consequence that the intercept corresponds to the mean value of the first group. … No, R does not center numerical variables automatically for ANCOVA. You have to do this by hand. …

My question is based on this response which showed which lme4::lmer model corresponds to a two-way repeated measures ANOVA: require(lme4) set.seed(1234) d <- data.frame( y = rnorm(96), subjec …

That is quite simple: For your data, estimating the correlation, doesn't improve the fit at all. Hence, the p-values need to be identically. > deviance(ml3) [1] 10910.52 > deviance(ml2) [1] 10910.52 …

I think that the main issue is that you need to increase the number of possible iterations the optimizer is allowed to have (in my humble opinion the default of 10,000 is a little low). This is what i …

As I said in my comment, I expect the problem to be one of additional random effects parameters in model compared to in modelT0 + modelT1 (specifically corraletions). Hence I would first check for th …

As the Miller & Chapman (2001) paper explains in great detail, using a covariate that is correlated with the independent variables is usually a bad idea. Hence simply adding study to the model will le …

First of all, you really need to make sure, that adding a covariate makes any sense. Adding a covariate only makes sense, if the covariate is not correlated with the independent variable (i.e., if the …

I would like to run a confirmatory factor analysis (which essentially is a structural equation model) in R testing this. There are at least two mature packages of doing so sem and openMX. …