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Dear R friends. I´m trying to fit a Logistic Regression using glm( family='binomial'). Here is the model: *model<-glm(f_ocur~altitud+UTM_X+UTM_Y+j_sin+j_cos+temp_res+pp, offset=(log(1/off)), data=mydata, family='binomial')* mydata has 76820 observations. The response variable f_ocur) is a 0-1. This data is a SAMPLE of a bigger dataset, so the idea of setting the offset is to account that the data used here represents a sample of the real data to be a...

...andom subsets and then obtaining the 7 estimated parameters by dropping one subset at a time from the dataset. Then I can estimate uncertainty of the parameters. I understand the procedure but I´m unable to do it in R. This is the model that I´m fitting:*glm(f_ocur~altitud+UTM_X+UTM_Y+j_sin+j_cos+temp_res+pp+offset(log(1/off)), data=mydata, family='binomial')* Does anyone have an idea of how can I make this possible? I´d really appreciate if someone could help me with this. Thank you in advance. P.S. More information can be added if needed. Best regards. Lucas. [[alternative HTML v...

...39;binomial', *offset=T*), (I know offset is a vector of values) My doubt is about predicting the values on a new data. Does the predict() function considers the offset? o should I especified something? Here is the model I´m using: *model<-stepAIC(glm(f_ocur~altitud+UTM_X+UTM_Y+j_sin+j_cos+temp_res+pp, offset=(log(1/offset)) data=data, family='binomial', ), direction='both')* Now, I want to get the estimated values, how should I do it considering that I´m stablishing an offset value in the model? If anyone can help me, i´d really appreciate it. Thank you. Lucas. [[alte...