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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