Generalized linear mixed effects model cannot be optimized. Log transform followed by back trans?

[batecy36]

Hello,

First time poster. I am using proc glimmix for a GLMM and a binomial distribution. The model cannot be optimized even when I set the maxopt=100. I was thinking that my next step would be to use a transformation on my response variable and then back transform it. I would use normal distribution when doing this obviously. Does this should like a reasonable approach here? What type of transformation would I use here being its a binary variable (1 or 0) and what's the added code I need? Currently I have:

proc glimmix data=obj_1_emergence plots=residualpanel maxopt=100;
class Inoculum Moisture Fungicide Block Rep;
model Emergence=Inoculum|Moisture|Fungicide/ddfm=kr dist=normal;
random Block Block*Inoculum Rep(Block*Inoculum*Moisture);
lsmeans Inoculum|Moisture|Fungicide/lines;
lsmeans Fungicide*Inoculum/lines slicediff=Inoculum slicediff=Fungicide plots=(mean(clband connect sliceby=Fungicide));
run;

Thanks,

T

[Rick_SAS]

If your response variable is 0/1, then you have a BINARY response and should use the DIST=BINARY option. (The DIST=BINOMIAL option is often used for events/trials syntax.) 

Regarding your idea, every transformation of a binary variable will result in another binary variable, so, no, you cannot use a transformation to somehow convert the problem to one that can use DIST=NORMAL.

[batecy36]

So I actually have a split-split plot with subsampling, 6 per true replicate. I think I should be able to convert to a decimal between 0 and 1 by summing my 0 and 1's and dividing by 6. I then could run this as normally distributed and do a log transformation if need be? I attempted to use DIST=BINARY to no avail. The model will not be optimized unless I run it as normally distributed. I understand it is not ideal but I'm not sure what the better alternative is.

[Rick_SAS]

Did you try event/trial syntax?

Define Success = sum of 1s

Define nTrials = 6

Then use

MODEL Success/nTrials = .... / dist=binomial;