Stefano,

I was aware of this option.  I was assuming it was not ok to do fit +/- 1.96
se when you requested probabilities.  If this is legitimate then all the
better.

Thanks!
Troy

On 6 August 2010 22:25, Guazzetti Stefano <Stefano.Guazzetti@ausl.re.it>wrote:

> a closer look to the help on predict.glm will reveal that the function
> accepts a 'type' argument.
> In you case 'type = response' will give you the results in probabilities
> (that it seems to be what you are looking for).
> There also is an example on use of the 'type' argument at the end of the
> page.
>
> Stefano
>
> -----Messaggio originale-----
> Da: r-help-bounces@r-project.org
> [mailto:r-help-bounces@r-project.org]Per conto di Troy S
> Inviato: Friday, August 06, 2010 6:31 PM
> A: Michael Bedward
> Cc: r-help@r-project.org
> Oggetto: Re: [R] Confidence Intervals for logistic regression
>
>
> Michael,
>
> Thanks for the reply.  I believe Aline was sgiving me CI's on coefficients
> as well.
>
> So c(pred$fit + 1.96 * pred$se.fit, pred$fit - 1.96 *
> pred$se.fit) gives me the CI on the logits if I understand correctly?
>  Maybe
> the help on predict.glm can be updated.
>
> Thanks!
>
> On 6 August 2010 01:46, Michael Bedward <michael.bedward@gmail.com> wrote:
>
> > Sorry about earlier reply - didn't read your email properly (obviously :)
> >
> > You're suggestion was right, so as well as method for Aline below,
> > another way of doing the same thing is:
> >
> > pred <- predict(y.glm, newdata= something, se.fit=TRUE)
> > ci <- matrix( c(pred$fit + 1.96 * pred$se.fit, pred$fit - 1.96 *
> > pred$se.fit), ncol=2 )
> >
> > lines( something, plogis( ci[,1] ) )
> > lines( something, plogis( ci[,2] ) )
> >
> >
> >
> > On 6 August 2010 18:39, aline uwimana <rwanuza@gmail.com> wrote:
> > > Dear Troy,
> > > use this commend, your will get IC95% and OR.
> > >
> > >  logistic.model <- glm(formula =y~ x1+x2, family = binomial)
> > > summary(logistic.model)
> > >
> > > sum.coef<-summary(logistic.model)$coef
> > >
> > > est<-exp(sum.coef[,1])
> > > upper.ci<-exp(sum.coef[,1]+1.96*sum.coef[,2])
> > > lower.ci<-exp(sum.coef[,1]-1.96*sum.coef[,2])
> > >
> > > cbind(est,upper.ci,lower.ci)
> > >
> > > regards.
> > >
> > > 2010/8/6 Troy S <troysocks-twigs@yahoo.com>
> > >
> > >> Dear UseRs,
> > >>
> > >> I have fitted a logistic regression using glm and want a 95%
> confidence
> > >> interval on a response probability.  Can I use
> > >>
> > >> predict(model, newdata, se.fit=T)
> > >>
> > >> Will fit +/- 1.96se give me a 95% of the logit?  And then
> > >> exp(fit +/- 1.96se) / (exp(fit +/- 1.96se) +1) to get the
> probabilities?
> > >>
> > >> Troy
> > >>
> > >>        [[alternative HTML version deleted]]
> > >>
> > >> ______________________________________________
> > >> R-help@r-project.org mailing list
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> > >> http://www.R-project.org/posting-guide.html
> > >> and provide commented, minimal, self-contained, reproducible code.
> > >>
> > >
> > >        [[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.
> > >
> >
>
>        [[alternative HTML version deleted]]
>
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