untested, but something like this should get you what you want:
no.it <- 5
out <- vector("list", length=no.it)
for(i in 1:no.it){
mydata2 <- mydata[ sample(1:nrow(mydata), 76000/no.it) ,]
out[[i]] <- coef(
glm(f_ocur~altitud+UTM_X+UTM_Y+j_sin+j_cos+temp_res+pp+offset(log(1/off)),
data=mydata2, family='binomial') )
}
do.call(c, out)
Lucas wrote
> Dear R friends
>
> I´m interested into apply a Jackknife analysis to in order to quantify the
> uncertainty of my coefficients estimated by the logistic regression. I´m
> using a glm(family=binomial) because my independent variable is in 0 - 1
> format.
>
> My dataset has 76000 obs, and I´m using 7 independent variables plus an
> offset. The idea involves to split the data in lets say 5 random 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 version deleted]]
>
>
> ______________________________________________
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
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