2 level logit, 2 REs, large sample - log likelihood returns "NaN"
To clarify, the models were fit using the glmer cmd of the lme4 package. Specifically, the model with scripted as: proto <- glmer(hibpe ~ age + b + b_age + h + h_age + female + female_age + bxf + hxf + numwaves + dead + nodoctor + nohosp + (age| hhidpn), nAGQ =150, family=binomial, data=hrs_data, na.action =na.omit, verbose=TRUE) Best, Daniel
On Fri, Jun 10, 2011 at 4:08 AM, Daniel Adkins <deadkins at vcu.edu> wrote:
Hi, I am fitting a large (j=50K, i=9K) 2-level logit with random intercept and age slope and 14 covariates. Model estimates become stable at nAGQ>=150 (large, I know). Based on simpler models (random intercept only, random slope only, ordinary logit, etc) the solution looks sound. However, all the fit indices return a value of "NaN", which naturally stands for "not a number". Why is this? This model should yield a scalar log likelihood, no? Any advice would be appreciated. Thanks, Daniel -- Daniel E. Adkins, PhD Assistant Professor Center for Biomarker Research and Personalized Medicine School of Pharmacy Virginia Commonwealth University McGuire Hall, Room 216B 1112 East Clay Street Richmond, VA 23298
Daniel E. Adkins, PhD Assistant Professor Center for Biomarker Research and Personalized Medicine School of Pharmacy Virginia Commonwealth University McGuire Hall, Room 216B 1112 East Clay Street Richmond, VA 23298