Exercise

# Chocolate model with all coefficients random

Now that we have the effects coding stored with the `chocolate`

data, we are ready to fit a model where all the coefficients are normally distributed. In order to do that, we need to create the `rpar`

vector to input to `mlogit()`

. That's a bit tricky, so I've written the code for you, but you should run it to see how it works. Then, you are going to write the call to `mlogit()`

.

Instructions

**100 XP**

- The first two inputs are the model formula
`Selection ~ 0 + Brand + Type + Price`

and the data`chocolate`

. - The next input should be
`rpar = my_rpar`

which tells`mlogit()`

which coefficients we want to be normally distributed. - The last input should be
`panel = TRUE`

.