[R] Jackknife in Logistic Regression

[Frank Harrell]

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]]
> 
> 
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-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
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