
Hi there R-users,
 
I have relative abundance data for 13 mammal species that I collected at various sites that ranged 
in road density. I'm trying to determine the effect of road density on animal abundance across body sizes. 
For most species, I have data that was collected in one year but for a few species I have two years of 
complete data, to account for this I nested year (as a factor) within body size (also a factor). I'm trying to 
run three models in lmer; random intercept, random slope and random intercept and slope (see below), and then 
use AIC/BIC to select the best model. My first question is, have I coded these models correctly?

random intercept:

M1<-lmer(Log_Abundance ~ Log_RoadDensity + (1|fBodySize/fYear), data=mixed)

random slope:

M2<-lmer(Log_Abundance ~ Log_RoadDensity + (0+Log_RoadDensity |fBodySize:fYear), data=mixed)

random effects intercept and slope:

M3<-lmer(Log_Abundance ~ Log_RoadDensity + (Log_RoadDensity|fBodySize/fYear),data=mixed)


And second, when running the random intercept and slope model, it fails to converge. I have tried 
increasing the maximum number of iterations with (control=list(maxIter=1000)) but it still won't converge. Does 
anyone have any suggestions?

Thank you,

Bob




 		 	   		  
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