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 <dwinsemius@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]]

