standard error for lda()

Hi, didn't hear any response yet. want to give it another try..  
appreciate any suggestions.

John

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 at comcast.net>
To: array chip <arrayprofile at yahoo.com>
Cc: "r-help at r-project.org" <r-help at 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

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]]

David Winsemius, MD
West Hartford, CT