[R] logistic regression where the independant variable is a ratio

[Patrick Giraudoux]

Dear Lister,

I have collected data in 6 geographical areas on prevalence of a 
parasite in humans and in foxes. The results are expressed as a number 
of positive or negative cases in human and foxes in the following 
data.frame:

Pvtab <-
structure(list(posHum = c(3, 5, 3, 17, 0, 4), negHum = c(32631,
16293, 27988, 231282, 53215, 51046), posFox = c(18, 23, 18, 191,
12, 55), negFox = c(14, 24, 62, 105, 55, 43)), .Names = c("posHum",
"negHum", "posFox", "negFox"), row.names = c("zone 1", "zone 2",
"zone 3", "zone 4", "zone 5", "zone 6"), class = "data.frame")

I want to check a possible link between prevalences in humans (the 
reponse variable) and prevalences in foxes (the independant variable). I 
though about a logistic regression of the form:

pvFox<-Pvtab$posFox/(Pvtab$posFox+Pvtab$negFox) # computes the 
prevalence in foxes for each area

mod0<-mod0<-glm(cbind(Pvtab$posHum,Pvtab$negHum)~pvFox,family=binomial)

But in this cas the number of foxes that have been used to compute the 
prevalence estimate in foxes (pvFox) is deliberatly not taken into 
account in the model. I can hardly figure out how to do it (weighing the 
model with the square root of the number of fox in each area ?).

Any advise appreciated about how to model a prevalence as a response of 
another prevalence at best.

Patrick