On Thu, 19 Jun 2008, Rolf Turner wrote:
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.)
I would suggest rather computing confidence intervals on linear predictor
scale and transforming those to response scale. That way you will not get
e.g. negative values for probabilities in a logistic regression.
cheers,
Rolf Turner
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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