[Rd] Problem with confint function
peter dalgaard
pdalgd at gmail.com
Fri Feb 11 20:04:08 CET 2011
On Feb 11, 2011, at 19:25 , Kino Aguilar wrote:
> Hi,
>
> I am currently doing logistic regression analyses and I am trying to get
> confidence intervals for my partial logistic regression coefficients.
> Supposing I am right in assuming that the formula to estimate a 95% CI for a
> log odds coefficient is the following:
>
> log odds - 1.96*SE to log odds + 1.96*SE
>
> then I am not getting the right CI.
>
> For instance, this is a summary of my model:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -0.06106 0.29808 -0.205 0.8377
> pSusSD 0.21184 0.36886 0.574 0.5658
> pBenSD 1.20255 0.52271 2.301 0.0214 *
> pBarSD -0.08654 0.48749 -0.178 0.8591
> pSevSD 0.99759 0.44795 2.227 0.0259 *
>
> And this is are the corresponding CI when I call the confint function:
> 2.5 % 97.5 %
> (Intercept) -0.6548023 0.5264357
> pSusSD -0.4980888 0.9733975
> pBenSD 0.2665235 2.3495259
> pBarSD -1.0695945 0.8740359
> pSevSD 0.1877044 1.9747499
>
> Utilizing the formula I mentioned above, the correct CI for pSusSD would
> actually be:
>> .21184-1.96*.36886
> [1] -0.5111256
>> .21184+1.96*.36886
> [1] 0.9348056
>
> That is:
> 2.5 % 97.5 %
> pSusSD -0.5111256 0.9348056
>
> I am wondering if there is a bug in the code or if there is another way to
> calculate a 95% CI for a logistic regression coefficient that I am not aware
> of?
>
confint.glm computes using likelihood profiling (i.e., it inverts the likelihood ratio test criterion for a parameter). This is considered somewhat more accurate than the Wald approximation implied by +/- 1.96*SE. If you insist on the latter, try confint.default. (This *is* all on the help page for confint()!).
> Thanks!
>
> --
> All the best!,
> ~Joaquin A. Aguilar A. - aka Kino
>
> [[alternative HTML version deleted]]
>
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--
Peter Dalgaard
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com
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