David, thanks for your response, hope this stirs more... Ok, a simple code:
y<-as.factor(rnorm(100)>0.5) x1<-rnorm(100) x2<-rnorm(100) obj<-glm(y~x1+x2,family=binomial) predict(obj,type='response',se.fit=T) predict(obj,...) will give predicted probabilities in the "fit" element; and the associated estimated standard errors in the "se.fit" element (if I understand correctly). The predicted probability from logistic regression is ultimately a function of y and thus a standard error of it should be able to be computed. So one of my questions is whether we can use normal approximation to construct 95% CI for the predicted probabilities using standard errors, because I am not sure if probabilities would follow normal distribution? Now, if we try lda(): library(MASS) obj2<-lda(y~x1+x2) predict(obj2) where predict(obj2) produces posterior probabilities, the predicted class, etc. My question is whether it's possible to produce standard errors for these posterior probabilities? Thanks again. John ________________________________ From: David Winsemius <dwinsem...@comcast.net> Cc: "r-help@r-project.org" <r-help@r-project.org> Sent: Thursday, February 9, 2012 2:59 PM Subject: Re: [R] standard error for lda() On Feb 9, 2012, at 4:45 PM, array chip wrote: > Hi, didn't hear any response yet. want to give it another try.. appreciate > any suggestions. > My problem after reading this the first time was that I didn't agree with the premise that logistic regression would provide a standard error for a probability. It provides a standard error around an estimated coefficient value. And then you provided no further details or code to create a simulation, and there didn't seem much point in trying to teach you statistical terminology that you were throwning around in a manner that seems rather cavalier , .... admittedly this being a very particular reaction from this non-expert audience of one. > John > > > ________________________________ > > To: "r-help@r-project.org" <r-help@r-project.org> > Sent: Wednesday, February 8, 2012 12:11 PM > Subject: [R] standard error for lda() > > Hi, I am wondering if it is possible to get an estimate of standard error of > the predicted posterior probability from LDA using lda() from MASS? Logistic > regression using glm() would generate a standard error for predicted > probability with se.fit=T argument in predict(), so would it make sense to > get standard error for posterior probability from lda() and how? > > Another question about standard error estimate from glm(): is it ok to > calculate 95% CI for the predicted probability using the standard error based > on normal apprximation, i.e. predicted_probability +/- 1.96 * standard_error? > > Thanks > > John > [[alternative HTML version deleted]] > > ______________________________________________ > 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. > [[alternative HTML version deleted]] > > ______________________________________________ > 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. David Winsemius, MD West Hartford, CT [[alternative HTML version deleted]]
______________________________________________ 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.
