[R] Seeking help with LOGIT model

Dear all, I am fitting a LOGIT model on this Data... Data - structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 47, 58, 82, 100, 222, 164, 161, 70, 219, 81, 209, 182, 185, 104, 126, 192, 95, 245,

Re: [R] Seeking help with LOGIT model

Christofer Bogaso bogaso.christofer at gmail.com writes: Dear all, I am fitting a LOGIT model on this Data... snip --- glm(Data[,1] ~ Data[,-1], binomial(link = logit)) Call: glm(formula = Data[, 1] ~ Data[, -1], family = binomial(link = logit)) Coefficients: (Intercept)

Re: [R] Seeking help with LOGIT model

Thanks Ken for your reply. No doubt your english is quite tough!! I understand something is not normal with the 5th explanatory variable (se:2872.17069!) However could not understand what you mean by You seem to be getting complete separation on X5 ? Can you please be more elaborate? Thanks, On

Re: [R] Seeking help with LOGIT model

You should look up the Hauck-Donne phenomenon, which shows that with binomial GLMs, the standard error can grow faster than the effect size. Complete separation results, for example, when one predictor (or a combination of several predictors) perfectly predicts the response. Something like this