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Thanks Chris.

First of all, just wanted to understand a bit better the new syntax. In particular, the level-2 random component.
Are the numbers in square brackets 1[1] + 1[2]|USUBJID referring to the continuous outcomes (1 and 2)? The R2MLwiN guide suggests that the square brackets are only used ...

Hi Chris,
I'm using runMLwiN in R to estimate a mixed hierarchical model (2 continuous and 2 binary). The data is multilevel with patients (USUBJID) clustered within groups (STUDYID).
In the past I have been using successfully this code:

runMLwiN(Formula ="c(walk1, QOL1, probit(death12mo,denomb1 ...

Hi,
After a helpful discussion with Chris, here are some useful notes than may be of interest to other users:

- when prior variance is not definite positive, one may have to fix some of the covariance elements of the covariance matrices stored in C1096.

- This can be done via R2MLwiN by ...

Thanks Chris.
The GUI window now opens but the MLwiN is still crashing when trying to estimate the model by MCMC.
I will email you an example dataset.
Manuel

Hi Chris,
Thanks for the suggestion. When I included 'debugmode=TRUE', it did not open a MLwiN window? It should, right?
Anyway, I tried to estimate the model manually using MLwiN. I first estimated the model by IGLS to generate the starting values for the MCMC estimation, then went to C1096 and ...

Hi,

I'm estimating a multilevel mixed (2 continuous, 2 binary) model using R2MLwiN.
The model runs fine when estimated by RIGLS. However, when I try estimating the same model using MCMC, I get:
MCMC Error 0315: Prior variance matrix is not positive definite

The most common suggestion here in the ...

Bill,
Many thanks for your detailed response.
I will compare this approach with the latent Normal variable (Albert and Chib, 1993) and I will let you know.
Best,
Manny

Hi,

I'm trying to use R2MLwiN to write some BUGS code of a bivariate mixed model, but I'm getting the error below:

mod<- runMLwiN(Formula ="c(y1, probit(y2,cons)) ~ (0s|cons+treat) + (1s|cons.y1) + (2s|cons)",
D=c("Mixed", "Normal", "Binomial"), levID = c("cluster", "id"), indata = data ...

Thanks Chris!
Apologies for having missed the FP.cov component included in the object.

Dear Richard,
After estimating an hierarchical model using R2MLwiN, is there a way of directly extracting the covariance between two estimated (fixed) coefficients?
I am looking into the object from runMLwiN, say 'estimates' ----> estimates["estIGLS"]
and check the 2nd column (_FP_v) which I think ...