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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