R help - Jun 2008 - Pointwise Confidence Bounds on Logistic Regression

Hi all.  I hope I have my terminology right here...

For a simple lm, one can add ?pointwise confidence bounds? to a fitted line
using something like
>predict(results.lm, newdata = something, interval = "confidence")
(I'm following DAAG page 154-155 for this)

I would like to do the same thing for a glm of the logistic regression type,
for instance, the example in MASS pg 190-192 (available in the help page for
predict.glm).

However, predict.glm does not have the same kind of features as "plain
old"
predict, i.e. One cannot specify interval = "confidence"
>From what I've read, "pointwise confidence bounds" are
computed from the
SE's for each point.  However, I don't see quite where to extract this
information with a glm

So, is there an existing function that does what I am describing for a glm,
or can someone point me in the right direction to start writing my own?

TIA as always, Bryan
*************
Bryan Hanson
Professor of Chemistry & Biochemistry
DePauw University, Greencastle IN USA

On 19/06/2008, at 8:08 AM, Bryan Hanson wrote:
> Hi all.  I hope I have my terminology right here...
>
> For a simple lm, one can add ?pointwise confidence bounds? to a  
> fitted line
> using something like
>
>> predict(results.lm, newdata = something, interval =
"confidence")
>
> (I'm following DAAG page 154-155 for this)
>
> I would like to do the same thing for a glm of the logistic  
> regression type,
> for instance, the example in MASS pg 190-192 (available in the help  
> page for
> predict.glm).
>
> However, predict.glm does not have the same kind of features as  
> "plain old"
> predict, i.e. One cannot specify interval = "confidence"
	I guess that one reason for that is that prediction intervals
	rarely if ever make sense with generalized linear models.  So only
	one kind of interval is in effect possible.
>
>> From what I've read, "pointwise confidence bounds" are
computed
>> from the
> SE's for each point.  However, I don't see quite where to extract
this
> information with a glm
>
> So, is there an existing function that does what I am describing  
> for a glm,
> or can someone point me in the right direction to start writing my  
> own?
Use predict(<whatever>,type="response",se.fit=TRUE).  You get a
list
with
three components, the first two of which are the fitted values and their
standard errors.  (The third is the ``scale'' factor, usually/often  
equal to 1.)

	cheers,

		Rolf Turner
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