On Feb 9, 2012, at 6:30 PM, array chip wrote:
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?
Wouldn't it be a binomial distribution if you're dealing with
classification.
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?
The heuristic I use in situations like this: If the authors didn't
think this was a desirable feature, they probably had sensible reasons
for _not_ including it (or they decided that another method, such as
logistic regression, was better). I cannot think of a good metric for
probability along the line perpendicular to the "line of maximal
discrimination" for which I confess I cannot remember the accepted
name. If I were asked to come up with an estimate I would probably
revert to a bootstrap strategy.
Thanks again.
John
From: David Winsemius <dwinsem...@comcast.net>
To: array chip <arrayprof...@yahoo.com>
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]]
>
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
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> 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
David Winsemius, MD
West Hartford, CT
______________________________________________
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.
