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?

Thanks!

-- 
All the best!,
~Joaquin A. Aguilar A. - aka Kino

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