[R-sig-ME] In mer_finalize(ans) : gr cannot be computed at initial par (65)

[Sol Heber]

Dear R List –

I am trying to use a GLMM to analyse data on breeding success from crossing experiments between inbred lines of fruit flies but am having problems. 

My response variable is a proportion (proportion of eggs that hatched into adults), I have two fixed factors (one of them, “cross”, has 4 levels: inbred, 1st or 2nd generation hybrid, and outbred, while the other fixed factor, “line”, has 3 levels: the initial two lines of fruit flies used for the experiments and the resulting hybrid line), and two random terms – female and male origin (to control for the fact that eggs of the same pair are not independent). 

My experimental design therefore looks something like this (resulting in missing cells in some of the combinations, e.g. there would be no data for the interaction inbred*AB, because in the inbred category there is no mix of the two lines):

Cross				Lines		
Inbred		AA				BB		
Hybrid F1	A1A2		AB		B1B2
Hybrid F2	A1A2		AB		B1B2
Outbred	        AA				BB

I have been using the following commands: 

> data<-read.table(file="data.txt",header=TRUE)
> attach(data)
> names(data)
 [1] "cross"     "line"      "forigin"   "morigin"  
 [6] "eggs" 	  "adults" 

> library(lme4)

> y<-cbind(adults,(eggs-adults))

> model1<-lmer(y~cross*line+(1|forigin)+(1|morigin),family=binomial)

But I get the error message: In mer_finalize(ans) : gr cannot be computed at initial par (65)

I suppose that I receive this error message because the experimental design is unbalanced, because as soon as I take out the interaction between the explanatory variables, R seems to be able to run the model. But it is precisely the interaction between „cross“ and „line“ that I am interested in.

So I have tried a different approach, by coding the origin of each individual fly: instead of using “cross” and “line” I have tried using maternal grandmother, maternal grandfather, paternal grandmother and paternal grandfather, which combines both the information of “cross” and “line” and should avoid the problem of the unbalanced design.

Again, after attaching the data I have been using the following commands:

> names(data)
 [1] "mat_granm"  "mat_granf" "pat_granm"  "pat_granf"  "forigin"  "morigin"  "eggs"  "adults"   

> y<-cbind(adults,eggs-adults)

> model1<-lmer(y~mat_granm*mat_granf*pat_granm*pat_granf+(1|forigin)+(1|morigin),family=binomial)

This again doesn’t work, but instead of getting an error message, R hangs itself up every time I attempt the analysis (I left it over night to see if it would just take that long, but the program doesn’t react).

Any help would be greatly appreciated, and I would also be happy to make the data available for this purpose.

Sol



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