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 let’s 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]]
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 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 let?s 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]] > > > ______________________________________________> R-help@> mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Jackknife-in-Logistic-Regression-tp4649520p4649555.html Sent from the R help mailing list archive at Nabble.com.
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 let?s 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]]
>
>
> ______________________________________________
> R-help@
> mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
View this message in context:
http://r.789695.n4.nabble.com/Jackknife-in-Logistic-Regression-tp4649520p4649563.html
Sent from the R help mailing list archive at Nabble.com.