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 forum [http://www.bris.ac.uk/cmm/software/supp ... html#c1096] is to set the off-diagonal elements (covariances) to zero.

I tried to do that using R2MLwiN by specifing the starting values for the level-2 covariance matrix (code below). However, the error persists. I wonder whether the specification below is wrong or whether setting covariance=0 does not fix my problem?

init<-diag(4)

model<- runMLwiN(Formula ="c(walk1, QOL1, probit(death,denomb1), probit(NYHA_bin,denomb2)) ~

(0s|cons+CRT+SEX+CRTMALE) + (1s|cons.walk1 + cons.QOL1) + (2s|cons)",

D=c("Mixed","Normal","Normal","Binomial","Binomial"),

levID = c("STUDYID", "USUBJID"), indata = data.cca,

estoptions=list(x64=TRUE, EstM=1, mcmcMeth = list(fixM = 1, residM = 1,

Lev1VarM = 2, OtherVarM=2), startval=list(RP.v=init)), MLwiNPath = 'C:/Program Files (x86)/MLwiN v2.29')

init<-diag(4)

model<- runMLwiN(Formula ="c(walk1, QOL1, probit(death,denomb1), probit(NYHA_bin,denomb2)) ~

(0s|cons+CRT+SEX+CRTMALE) + (1s|cons.walk1 + cons.QOL1) + (2s|cons)",

D=c("Mixed","Normal","Normal","Binomial","Binomial"),

levID = c("STUDYID", "USUBJID"), indata = data.cca,

estoptions=list(x64=TRUE, EstM=1, mcmcMeth = list(fixM = 1, residM = 1,

Lev1VarM = 2, OtherVarM=2), startval=list(RP.v=init)), MLwiNPath = 'C:/Program Files (x86)/MLwiN v2.29')

Any help would be much appreciated.

Manny