similar to: Multiple CHOLMOD errors when attempting poisson glmm

Hi, I am getting a warning message when I am fitting a generalized mixed model (mod_2) and I don't understand why because when I add just an interaction factor the model works perfectly (mod_1). Does anyone know what it happpens ? Thanks, Aïda   > mod_1<-lmer(sur15~soeviv15_4plus+frviv15_4plus+frat_15death+dad_class_new+soeviv15_4plus:dad_class_new +frviv15_4plus:dad_class_new+

I have a pretty large sparse matrix of integers: > dim(tasa) [1] 91650 37651 I need to add one to it in order to take logs, but I'm getting the following error: > tasa = log(tasa + 1) CHOLMOD error: problem too large Error in asMethod(object) : Cholmod error `problem too large' I have 2 Gb of RAM, and the current workspace is barely 300mb. Is there any workaround to this?

I have a pretty large sparse matrix of integers: > dim(tasa) [1] 91650 37651 I need to add one to it in order to take logs, but I'm getting the following error: > tasa = log(tasa + 1) CHOLMOD error: problem too large Error in asMethod(object) : Cholmod error `problem too large' I have 2 Gb of RAM, and the current workspace is barely 300mb. Is there any workaround to this?

Dear R-users, I'm new to R, so my apologies if this question doesn't make sense. I've tried the following model in lmer, and it works perfectly: model<-lmer(aphids~densroot+zone+(1|zone/site), family=quasipoisson) But if I try the exact same model with a different variable, totmas, the model looks as follows: model<-lmer(aphids~totmas+zone+(1|zone/site), family=quasipoisson)

Hi, I am getting a warning message when I am fitting a generalized linear mixed model (m1.2 below). CHOLMOD warning: matrix not positive definite Error in objective(.par, ...) : Cholmod error `matrix not positive definite' at file:../Supernodal/t_cholmod_super_numeric.c, line 614 Any idea? Thanks for your help, Reza > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32

Hi, I am new to this list. I have a question regarding including both spatial and temporal random factors in lmer. These two are not nested, and an example of model I try to fit is model1<-lmer(Richness~Y+Canopy+Veg_cm+Treatment+(1|Site/Block/Plot)+(1|Year), family=poisson, REML=FALSE), where richness = integer Y & Treatment = factor Canopy & Veg_cm = numerical, continous