Dear Freesurfer Users,

I am trying different LME models to describe the time evolution of cortical thickness in anorexia patients. The LME tools provide a function (lme_mass_LR) to test whether a model with q+1 random effects is better (fits the data better) than one with q random effects, eg whether a model with random intercept and slope is better than one with only intercept random.

I have two questions:
1- is there a similar function to test whether the model with intercept as a random effect is better than the model with slope as a random effect (ie two models with the same number of random effects)? In another thread I read one should judge which random effect is to be chosen from the percentage of voxels where one has convergence. It sounds reasonable, but an explanation of why this should be so would be welcome.
2- does it make sense to use lme_mass_LR to test whether a model with 1 random effect is better than one with 0 random effects? I have it with something like:

LR_pval = lme_mass_LR(lhstats_0RE,lhstats_1RE,0);

And I get as a result LR_pval= 0.3 (a constant vector) which I do not know how to interpret: something wrong, this test does not make any sense, choose the 0 random effect model ...

Thanks in advance for your help.

Cheers,
Fabio