Exercise

# binomial family argument

The big difference between running a linear regression with `lm()`

and running a logistic regression with `glm()`

is that you have to set `glm()`

's `family`

argument to `binomial`

. `binomial()`

is a function that returns a list of other functions that tell `glm()`

how to perform calculations in the regression. The two most interesting functions are `linkinv`

and `linkfun`

, which are used for transforming variables from the whole number line (minus infinity to infinity) to probabilities (zero to one) and back again.

A vector of values, `x`

, and a vector of probabilities, `p`

, are available.

Instructions

**100 XP**

- Examine the structure of the
`binomial()`

function.*Notice that it contains two elements that are functions,*`binomial()$linkinv`

, and`binomial()$linkfun`

. - Call
`binomial()$linkinv()`

on`x`

, assigning to`linkinv_x`

. - Check that
`linkinv_x`

and`plogis()`

of`x`

give the same results with`all.equal()`

. - Call
`binomial()$linkfun()`

on`p`

, assigning to`linkfun_p`

. - Check that
`linkfun_p`

and`qlogis()`

of`p`

give the same results.