- Estimate model in R: glm(y ~ x1 + x2, family = binomial, data = data_train)
fit <- glm(as.factor(is_recid) ~ age + priors_count,
family = binomial,
data = data_train)
cat(paste(capture.output(summary(fit))[11:14], collapse="\n"))
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.101001 0.097597 11.28 <2e-16 ***
## age -0.049831 0.002861 -17.42 <2e-16 ***
## priors_count 0.159982 0.008236 19.43 <2e-16 ***
- R output shows log odds: e.g., a one-unit increase in
age is associated with an increase in the log odds of is_recid by -0.05 units
- Difficult to interpret.. much easier to use predicted probabilities