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 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]] > > > ______________________________________________ > 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. ______________________________________________ R-help@r-project.org 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.
