Logistic regression is a generalized linear model for binomially distributed data which uses a logit function link.
Stata offers multiple commands for running a logistic regression. glm and logit report log-odds coefficients while logistic reports odds ratios. logit is just a shortcut for glm to save typing out the family and link.
glm x_d1 y z, family(binomial) link(logit)
logit x_d1 y z
logistic x_d1 y z
R uses glm() for logistic regression.
example_model2 <-
glm(x_d1 ~ y + z,
family = binomial(link = "logit"),
data = example_data)
summary(example_model2)To obtain odds ratios, exponentiate the log-odds coefficients and/or their confidence intervals:
exp(coef(example_model2))
exp(confint(example_model2))