Re: [R] Pointwise Confidence Bounds on Logistic Regression

From: Prof Brian Ripley <>
Date: Wed, 18 Jun 2008 22:32:48 +0100 (BST)

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", 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,        
Professor of Applied Statistics,
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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