`ddply` fails to apply logistic regression (GLM) by group to my dataset - r

Question

I'm working out the LD50 (lethal dosage) for multiple populations from different experiments using the MASS package. It's simple enough when I subset the data and do one at a time, but I'm getting an error when I use ddply. Essentially I need an LD50 for each population at each temperature.
My data looks somewhat like this:
# dput(d)
d <- structure(list(Pop = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L), .Label = c("a", "b", "c"), class = "factor"), Temp = structure(c(1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("high", "low"), class = "factor"),
Dose = c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L), Dead = c(0L,
11L, 12L, 14L, 2L, 16L, 17L, 7L, 5L, 3L, 17L, 15L, 9L, 20L,
8L, 19L, 7L, 2L, 20L, 14L, 9L, 15L, 1L, 15L), Alive = c(20L,
9L, 8L, 6L, 18L, 4L, 3L, 13L, 15L, 17L, 3L, 5L, 11L, 0L,
12L, 1L, 13L, 18L, 0L, 6L, 11L, 5L, 19L, 5L)), .Names = c("Pop",
"Temp", "Dose", "Dead", "Alive"), class = "data.frame", row.names = c(NA,
-24L))
The following works fine:
d$Mortality <- cbind(d$Alive, d$Dead)
a <- d[d$Pop=="a" & d$Temp=="high",]
library(MASS)
dose.p(glm(Mortality ~ Dose, family="binomial", data=a), p=0.5)[1]
But when I put this into ddply I get the following error:
library(plyr)
d$index <- paste(d$Pop, d$Temp, sep="_")
ddply(d, 'index', function(x) dose.p(glm(Mortality~Dose, family="binomial", data=x), p=0.5)[1])
Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1
I can get the right LD50 when I use a proportion but can't figure out where I've gone wrong with my approach (and had already written this question).

Answer 1

Perhaps this will amaze you. But if you choose to use formula
cbind(Alive, Dead) ~ Dose
instead of
Mortality ~ Dose
the problem will be gone.
library(MASS)
library(plyr)
## `d` is as your `dput` result
## a function to apply
f <- function(x) {
fit <- glm(cbind(Alive, Dead) ~ Dose, family = "binomial", data = x)
dose.p(fit, p=0.5)[[1]]
}
## call `ddply`
ddply(d, .(Pop, Temp), f)
# Pop Temp V1
#1 a high 2.6946257
#2 a low 2.1834099
#3 b high 2.5000000
#4 b low 0.4830998
#5 c high 2.2899553
#6 c low 2.5000000
So what happened with Mortality ~ Dose? Let's set .inform = TRUE when calling ddply:
## `d` is as your `dput` result
d$Mortality <- cbind(d$Alive, d$Dead)
## a function to apply
g <- function(x) {
fit <- glm(Mortality ~ Dose, family = "binomial", data = x)
dose.p(fit, p=0.5)[[1]]
}
## call `ddply`
ddply(d, .(Pop, Temp), g, .inform = TRUE)
#Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1
#Error: with piece 1:
# Pop Temp Dose Dead Alive Mortality
#1 a high 1 0 20 20
#2 a high 2 11 9 9
#3 a high 3 12 8 8
#4 a high 4 14 6 6
Now we we see that variable Mortality has lost dimension, and only the first column (Alive) is retained. For a glm with binomial response, if the response is a single vector, glm expects 0-1 binary or a factor of two levels. Now, we have integers 20, 9, 8, 6, ..., hence glm will complain
Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1
There is really no way to fix this issue. I have tried using a protector:
d$Mortality <- I(cbind(d$Alive, d$Dead))
but it still ends up with the same failure.