Exercise

# Choosing default models

MICE creates a separate imputation model for each variable in the data. What kind of model it is depends on the type of the variable in question. A popular way to specify the kinds of models we want to use is set a default model for each of the four variable types.

You can do this by passing the `defaultMethod`

argument to `mice()`

, which should be a vector of length 4 containing the default imputation methods for:

- Continuous variables,
- Binary variables,
- Categorical variables (unordered factors),
- Factor variables (ordered factors).

In this exercise, you will take advantage of `mice`

's documentation to view the list of available methods and to pick the desired ones for the algorithm to use. Let's do some model selection!

Instructions

**100 XP**

- In the RDocumentation returned by
`?mice`

, there's a table containing the keyword for each method. - Impute
`biopics`

data with`mice()`

using the following default methods, in this order: classification and regression trees, linear discriminant analysis, predictive mean matching, proportional odds model. - Print
`biopics_multiimp`

to see which method was used for which variable.