# Calculation of log-logit or log-probit models according to Finney using R

I'm trying to implement the logit/probit model derivation as introduced by Finney using sample data from http://dge.stanford.edu/SCOPE/SCOPE_12/SCOPE_12.html, chapter 6 (this links to a pdf), page 130, table 6.3. Results are similar but not identical. Finney calculated LD50 = 4.85.

Here is the source code and output of my R program:

# Finney, 1952
dosis <- c(2.6,3.8,5.1,7.7,10.2)
nges <- c(50,48,46,49,50)
nok <- c(6,16,24,42,44)

edx <- function(dosis=NA, nges=NA, nok=NA, lk='logit') {
require(MASS)
# weights w according to Finney 1952:
p <- nok/nges
q <- 1-p
w <- nges*p*q

# logit/probit model
edx.data <- data.frame(dosis, nges, nok)
glm.logit <- glm(cbind(nok,nges-nok) ~ dosis, family=binomial(lk), data=edx.data, weights=w)

# Calculation of EDx data
r <- dose.p(glm.logit,p=seq(0.1,0.9,0.2))

# Statistical summary
d <- data.frame(x=c(NA,NA,NA,NA))
rownames(d) <- c('Deg. of freedom','Deviance','1-chi.square','Significant difference between fits and observations')
d$x <- df.residual(glm.logit) d$x <- deviance(glm.logit)