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