# GLMER and Model is nearly unidentifiable: very large eigenvalue

I'm working on a logistic regression analysis using the lme4 package and function glmer. I built the following model:

results<-glmer(R0A1~MP_Scaled*Season1+MPHW_Scaled*Season1+HW_Scaled*Season1+YP_Scaled*Season1+AG_Scaled*Season1+Shrub_Scaled*Season1+(1|ID)+(1|Site),data=animals,family=binomial)


MP_Scaled, MPHW_Scaled, etc. are continuous variables and Season1 is a categorical variable. My approach is to understand whether the selection of certain habitat types differs across seasons A,B, and C.

I've re-scaled the continuous variables by dividing each linear distance (m) by 100-m. I ran this same analysis in SAS and the model converged. Is there a way to change the convergence criteria in the glmer model? In SAS, my model is specified as the following:

PROC GLIMMIX DATA=STUDYAREA;


CLASS ID SITE SEASON1; MODEL R0A1 = MP_SCALED|SEASON1 MPHW_SCALED|SEASON1 HW_SCALED|SEASON1 YP_SCALED|SEASON1 AG_SCALED|SEASON1 SHRUB_SCALED|SEASON1 / DIST=BINOMIAL LINK=LOGIT SOLUTION ODDSRATIO; RANDOM ID / TYPE = VC; RANDOM SITE / TYPE = VC; NLOPTIONS GCONV=0; RUN;

The NLOPTIONS GCONV=0 is used to continue the estimation until the max gradient is sufficiently small, which is done by setting the GCONV=0. Thanks for the assistance!

• This post is practically unreadable without formatting--the system is misinterpreting some of the special characters. Please edit it and use the tools above the textbox to format code and error messages.
– whuber
Jul 27 '15 at 22:13
• @whuber, does that work? Sorry for the issues in posting earlier. I can't seem to find an easy way to post my R code/output without issues in formatting. Jul 27 '15 at 22:30

     glmerControl(...,

or change the optimization parameters via the optCtrl argument, although this is a bit obscure for the bobyqa optimizer (help("bobyqa",package="minqa")). Check help("convergence",package="lme4") for more information about convergence, among other things the fact that
If you get similar answers from lme4 and PROC GLIMMIX I would conclude that the warning is a false positive.