## Lecture

## R content

### Linear models

`lm(Y ~ x + x1 + x2, data = data)`

, with three independent predictors
`lm(Y ~ x*x1, data = data)`

, with an interaction effect
`lm(Y ~ x + x1 + x:x1, data = data)`

, with an interaction effect, written more verbosely
`lm(Y ~ ., data = data)`

, to include all other columns as predictors

### Logistic regression

`glm(Y ~ x + x1 + x2, data = data, family = 'binomial')`

, with three independent predictors
`glm(Y ~ ., data = data, family = 'binomial')`

, to include all other columns as predictors
- Code to extract relevant information for plotting:

```
model <- glm(Y ~ ., data = data, family = 'binomial')
plot.data <- tibble(x = model$linear.predictors,
y = model$fitted.values,
response = data$Y)
```