search for: empinf

...uot;influence(data, statistic,...)". This function aims to "calculate empirical influence values and related quantities", and is part of the Resample library that I cannot find for R. However, 2 similar functions are available in R: - the lm.influence(model, ...) function, - the empinf(data, statistic,...)" function. I didn't manage to use the lm.influence() function correctly, because it needs a linear model as input (lm, glm), and what I have as input is a function (I don't know well R/S-plus languages, so I may be mistaken, but I believe lm.influence() is no...

...ssion coeficients. My problem is that I can not compute bca intervals, I get an error message > bca.ci(blm8901,index=1) Error in if (!all(rk > 1 & rk < R)) warning("Extreme Order Statistics used as Endpoints") : missing value where logical needed The problem is the empinf.reg function that is returning Na when performing a glm regression, statement beta <- coefficients(glm(t ~ X))[-1] I don't see anything strange in t or X, but I'm missing something for sure. Can someone give me an hint on this ? Thanks EJ

Dear All, I am trying to use the bca.ci function on a boot.out object which consists a few NaN's and I want to ignore those NaN's, and get a ci only for the "normal" values. boot.out$t has R number of values for 3000 different statistics, so when I use boot.ci(boot.out, index=i) and i happens to be a column in boot.out$t with some NaN's in there I get an error message. I

...components of a parametric bootstrap object. > > When I pass the boot object to the boot.ci function I can get CIs of > types = norm, basic, and perc on each parameter estimate. > > However, bca CIs are not returned and there is the following error > message: > > Error in empinf(boot.out, index = index, t = t.o, ...) : > Influence values cannot be found from a parametric bootstrap > > To knowledge I should be able to get bca CIs on a parametric bootstrap > object. > > Any suggestions? > > Nathan > > > Nathan Leon Pace, MD, MStat Work:n.l...

Dear All, Anyone has some idea how to implement 632 estimator and leave-one out bootstraping method by using boot package. I know the bootstrap package has this function, but it sounds not very flexible for my project. Thanks, Jim