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There are no missing values in quants, resi.chains, chains or X.
predmin = 1.71; predamx = 2.83

If I run the syntax you indicated here, I get the following error message:

I currently have a model wich I ran in MCMC, for which I want to compare the effects of 2 interventions with a control condotion. So I have 2 dummies, Intervention1, and Intervention2 (both compared to those with no intervention). My hypothesis is that there is actually no difference between these 2...

Hi Chris,

I was able to run this syntax now. Can you indicate what you want me to check exactly? As indicated, it is not quite clear to me what I should be looking for here...

Thank you, I will look into this. I also read something about "scaling" (Fielding, 2004, quality & quantity 38: 425-433, Scaling for residual variance components of ordered category responses in generalized linear mixed multilevel models). Do you have an idea about whether this is some...

Suppose I would want to say something about "explained variance" at level 1 and level 2 in my models, after adding a bunch of variables. For instance, I first run a model with binary outcome Y with only cons (and variance at individual and school level). Then I run a model with the same ou...

I have to be honest: I cannot read all of this. I get an error message again: > quants <- apply(tval, 1, function(x) quantile(x, c(probs[1], 0.5, probs[2]))) Error in quantile.default(x, c(probs[1], 0.5, probs[2])) : object 'probs' not found Called from: quantile.default(x, c(probs[1], 0.5, probs[2]...

Thank you for your reply.
In this first inspection, I don't see odd patterns such as missing data. Do you have any other suggestions?

Disclaimer: I am not very experienced in either R or multilevel analyses. I am currently wrestling my way through the R2MLwin manual, examples online etc to familiarize myself with the package. I try to apply the info in the manual to my own data. So far, I have been able to google myself out of any...

I want to examine a) How much variance in my ordinal outcome variable is at individual level (level 1) vs. classroom level (level 2) vs school level (level 3) b) How much variance at each of these levels is explained by adding certain predictors to my model. With "normal" multilevel models...

Thank you for your response!
I have worked a bit with R but am certainly no expert, therefore I was wondering whether there might be easier alternatives.
If I read your message, it seems like that may not be the case.
So I will try to get re-acquainted with my old enemy R