Eik Vettorazzi wrote:
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.
The approach of anti-logging a coefficient only works in the simple case
in which the variable is binary (or a one-unit change is of interest and
the variable is linear), doesn't interact with another variable, and the
reference cell desired equals the reference cell coded.
Frank
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
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