Logistic Regression in R – Understanding Odds Ratio

logisticodds-ratior

I'm trying to undertake a logistic regression analysis in R. I have attended courses covering this material using STATA. I am finding it very difficult to replicate functionality in R. Is it mature in this area? There seems to be little documentation or guidance available. Producing odds ratio output seems to require installing epicalc and/or epitools and/or others, none of which I can get to work, are outdated or lack documentation. I've used glm to do the logistic regression. Any suggestions would be welcome.

I'd better make this a real question. How do I run a logistic regression and produce odds rations in R?

Here's what I've done for a univariate analysis:

x = glm(Outcome ~ Age, family=binomial(link="logit"))

And for multivariate:

y = glm(Outcome ~ Age + B + C, family=binomial(link="logit"))

I've then looked at x, y, summary(x) and summary(y).

Is x$coefficients of any value?

Best Answer

if you want to interpret the estimated effects as relative odds ratios, just do exp(coef(x)) (gives you $e^\beta$, the multiplicative change in the odds ratio for $y=1$ if the covariate associated with $\beta$ increases by 1). For profile likelihood intervals for this quantity, you can do

require(MASS)
exp(cbind(coef(x), confint(x)))

EDIT: @caracal was quicker...

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