search for: logexposure

...<- paste("logexp(", days, ")", sep="") structure(list(linkfun = linkfun, linkinv = linkinv, mu.eta = mu.eta, valideta = valideta, name = link), class = "link-glm") } # Modified binomial family function (that allows logexp link function) logexposure<-function (link="logexp",ExposureDays) { variance <- function(mu) mu * (1 - mu) validmu <- function(mu) all(mu > 0) && all(mu < 1) dev.resids <- function(y, mu, wt) .Call("binomial_dev_resids", y, mu, wt, PACKAGE = "stats")...

...main effects that were retained following the stepwise algorithm) in the final model or should I delete it as well and move on? Or should I retain the main effect as well? To picture it, the initial model to which I called stepAIC is: Call: glm(formula = S ~ FR + Date * age + I(age^2), family = logexposure(ExposureDays = DATA$int), data = DATA) and the final one: Call: glm(formula = S ~ FR + Date + I(age^2), family = logexposure(ExposureDays = DATA$int), data = DATA) Thanks very much in advance for your thoughts and suggestions, Caspar Caspar Hallmann MSc Student WUR The Netherland...

I apologize for my earlier posting that, unbeknownst to me before, apparently was not in the correct format for this list. Hopefully this attempt will go through, and no-one will hold the newbie mistake against me. I could really use some help in writing a new glm link function in order to run an analysis of daily nest survival rates. I've struggled with this for weeks now, and can at least