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Whoops, sorry, whilst creating the example data I identified the cause of the ‘type builtin not subsettable’ was related to my rookie mistake naming my dataframe ‘exp’ (doh!).
However things still don’t seem right: the mixed function now runs, but is taking an extremely long time to run compared with glmer, and hasn’t completed calculating P-values after several hours on my full dataset. It also produces a lot of non-convergence warnings.
Here’s an example data subset and code. The mixed function does eventually complete calculations on this data, but still comes up with the non-convergence warnings. It would be great if you could let me know if it’s working as expected or not. I hope I haven’t done anything else silly (I have checked everything I can think of). Thanks very much!!
#R version 3.4.3 (2017-11-30) -- "Kite-Eating Tree" #packages library(lme4) library(afex) #example data zz<-"factor1 factor2 subject abundance max A a 1 659 658 A a 2 1408 1407 A b 1 826 789 A b 2 1348 1271 B a 3 242 242 B a 4 487 480 B b 3 605 585 B b 4 618 599 C a 5 382 346 C a 6 613 598 C b 5 370 336 C b 6 1002 963" df <- read.table(text=zz, header = TRUE) #factors df$factor1<-as.factor(df$factor1) df$factor2<-as.factor(df$factor2) df$subject<-as.factor(df$subject) #create index variable df$index<-df$max/df$abundance #lme4::glmer mod1<-glmer((index*abundance)~factor1*factor2+(1|subject), data=df, family=poisson(link="log"), offset=(log(df$abundance))) #afex::mixed mod2<-mixed((index*abundance)~factor1*factor2+(1|subject), data=df, family=poisson(link="log"), offset=(log(df$abundance)), method="PB")
