Dear R users,
I've notice that there are two ways to conduct a binomial GLM with binomial
counts using R. The first way is outlined by Michael Crawley in his
"Statistical Computing book" (p 520-521):
>dose=c(1,3,10,30,100)
>dead = c(2,10,40,96,98)
>batch=c(100,90,98,100,100)
>response = cbind(dead,batch-dead)
>model1=glm(y~log(dose),binomial)
>summary(model1)
Which returns (in part):
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.5318 0.4381 -10.35 <2e-16 ***
log(dose) 1.9644 0.1750 11.22 <2e-16 ***
Null deviance: 408.353 on 4 degrees of freedom
Residual deviance: 10.828 on 3 degrees of freedom
AIC: 32.287
Another way to do the same analysis is to reformulate the data, and use GLM
with weights:
>y1=c(rep(0,5),rep(1,5))
>dose1=rep(dose,2)
>number = c(batch-dead,dead)
>data1=as.data.frame(cbind (y1,dose,number))
>model2=glm(y1~log(dose1),binomial,weights=number,data=data1)
>summary(model2)
Which returns:
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.5318 0.4381 -10.35 <2e-16 ***
log(dose1) 1.9644 0.1750 11.22 <2e-16 ***
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 676.48 on 9 degrees of freedom
Residual deviance: 278.95 on 8 degrees of freedom
AIC: 282.95
Number of Fisher Scoring iterations: 6
These two methods are similar in the parameter estimates and standard
errors, however the deviances, their d.f., and AIC differ. I take the
first method to be the correct one.
However, I'm really interested in conducting a GLM binomial mixed model,
and I am unable to figure out how to use the first method with the lmer
function from the lme4 library, e.g.
>model3=lmer(y~log(dose)+time|ID) # the above example data doesn't have
the random effect, but my own data set does.
Does anyone have any suggestions?
thanks,
chris
Thanks,
Chris O'Brien
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