Coefficients, OR and 95% CL

l.mod<-glm(menarche~age,binomial)

you will get odds-ratios by exponentiating the coefficients of this 
model, so

exp(coef(l.mod))

will do this job. You may notice that this will produce an "OR" for the 
intercept part as well - which is not interpretable.
For the confidence intervals for this odds ratio you have to 
exponentiate the borders of the "standard" confidence intervals for the 
log-odds-ratios,

exp(confint(l.mod))

hth.

Luciano La Sala schrieb:

Dear R-users,

After running a logistic regression, I need to calculate OR by 
exponentiating the coefficient, and then I need the 95% CL for the OR 
as well. For the following example (taken from P. Dalaagard's book), 
what would be the most straightforward method of getting what I need? 
Could anyone enlight me please?  
Thank you!
Lucho
 

summary(glm(menarche~age,binomial))
    

Call:
glm(formula = menarche ~ age, family = binomial)

Deviance Residuals:      Min        1Q    Median        3Q       Max  
-4.68654  -0.13049  -0.01067   0.09608   2.35254 
Coefficients:
            Estimate Std. Error z value Pr(>|z|)    (Intercept) 
-17.9175     1.7074  -10.49   <2e-16 ***
age           1.3549     0.1296   10.45   <2e-16 ***
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 974.31  on 703  degrees of freedom
Residual deviance: 223.95  on 702  degrees of freedom
  (635 observations deleted due to missingness)
AIC: 227.95

Number of Fisher Scoring iterations: 9