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
The standard errors and covariance matrix that automatically arise from
fitting the model already captures the uncertainties you seek, if I
understand.
Frank
Lucas wrote
> Dear R friends
>
> I´m interested into apply a Jackknife analysis to in order to quantify the
> uncertainty of my coefficient
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 independe
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