Dear R-list,
I have a data set from 20 pigs, each of which is tested at crossed 9 doses
(logdose -4:4) and 3 skin treatment substances when exposed to a standard
polluted environment. So there are 27 patches on each pig. The response is
irritation=yes/no.
I want to determine "equally effective 50% doses" (similar to old
LD50), and
to test the treatments against each other. I am looking for something like
dose.p in MASS generalized to lmer (or glmmPQL or whatever). The direct as
output by lmer are not useful, because saying "30% irritation with A and
40%
with B at dose xx" has less meaning than giving "equivalent effective
doses".
Dieter
----- Simulated data -----
library(lme4)
animal = data.frame(ID = as.factor(1:20), da = rnorm(1:20))
treat = data.frame(treat=c('A','B','C'),
treatoff=c(1,2,1.5),
treatslope = c(0.5,0.6,0.7))
gr = expand.grid(animal=animal$ID,treat=treat$treat,logdose=c(-4:4))
gr$resp = as.integer(treat$treatoff[gr$treat]+
treat$treatslope[gr$treat]*gr$logdose+
animal$da[gr$animal] + rnorm(nrow(gr),0,2) >0)
gr.lmer = lmer(resp ~ treat*logdose+(1|animal),data=gr,family=binomial)
summary(gr.lmer)
------- Output
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.9553 0.3074 3.11 0.0019 **
treatB 0.8793 0.3313 2.65 0.0079 **
treatC 0.5516 0.3077 1.79 0.0730 .
logdose 0.3733 0.0774 4.82 1.4e-06 ***
treatB:logdose 0.3081 0.1323 2.33 0.0198 *
treatC:logdose 0.2666 0.1249 2.13 0.0328 *
----- Goal
Value SD p
50% logdose (A-B) xx xx xx
50% logdose (A-C) yy yy yy
