Hi - I am doing some monte carlo simulations comparing bayesian (using
Plummer's jags) and maximum likelihood (using lmer from package lme4
by Bates et al).
I would like to know if there is a way I can flag nonconvergence and
exceptions. Currently the simulations just stop and the output reads
things like:
Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn, gr,
method = "L-BFGS-B", :
L-BFGS-B needs finite values of 'fn'
In addition: Warning message:
Leading minor of size 1 of downdated X'X is indefinite
Error in .local(object, ...) : Leading 2 minor of Omega[[1]] not
positive definite
In addition: Warning messages:
1: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH
in: "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, msMaxIter = 200,
2: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH
in: "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, msMaxIter = 200,
thanks for any help.
-eduardo
monte carlo simulations/lmer
2 messages · Eduardo Leoni, Douglas Bates
1 day later
On 8/13/05, Eduardo Leoni <e.leoni at gmail.com> wrote:
Hi - I am doing some monte carlo simulations comparing bayesian (using
Plummer's jags) and maximum likelihood (using lmer from package lme4
by Bates et al).
I would like to know if there is a way I can flag nonconvergence and
exceptions. Currently the simulations just stop and the output reads
things like:
Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn, gr,
method = "L-BFGS-B", :
L-BFGS-B needs finite values of 'fn'
In addition: Warning message:
Leading minor of size 1 of downdated X'X is indefinite
Error in .local(object, ...) : Leading 2 minor of Omega[[1]] not
positive definite
In addition: Warning messages:
1: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH
in: "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, msMaxIter = 200,
2: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH
in: "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, msMaxIter = 200,
As Rolf Turner indicated, you can wrap the call to lmer in try() to prevent breaking the loop on convergence failure. I'm not sure exactly what Bayesian analysis you are doing but you may want to look at the function mcmcsamp in versions 0.98-1and later of the Matrix package. It can take a fitted lmer object and create an MCMC sample from the posterior distribution of the parameters assuming a locally uniform prior on the fixed-effects parameters and the non-informative prior described by Box and Tiao for the variance-covariance matrices.