R: Imputation by classification and regression trees

mice.impute.cart {mice} | R Documentation

## Imputation by classification and regression trees

### Description

Imputes univariate missing data using classification and regression trees.

### Usage

    mice.impute.cart(y, ry, x, wy = NULL, minbucket = 5, cp = 1e-04, ...)

### Arguments

`y` | Vector to be imputed
`ry` | Logical vector of length `length(y)` indicating the the subset `y[ry]` of elements in `y` to which the imputation model is fitted. The `ry` generally distinguishes the observed (`TRUE`) and missing values (`FALSE`) in `y`.
`x` | Numeric design matrix with `length(y)` rows with predictors for `y`. Matrix `x` may have no missing values.
`wy` | Logical vector of length `length(y)`. A `TRUE` value indicates locations in `y` for which imputations are created.
`minbucket` | The minimum number of observations in any terminal node used. See `rpart.control` for details.
`cp` | Complexity parameter. Any split that does not decrease the overall lack of fit by a factor of cp is not attempted. See `rpart.control` for details.
`...` | Other named arguments passed down to `rpart()`.

### Details

Imputation of `y` by classification and regression trees. The procedure is as follows:

1. Fit a classification or regression tree by recursive partitioning;

2. For each `ymis`, find the terminal node they end up according to the fitted tree;

3. Make a random draw among the member in the node, and take the observed value from that draw as the imputation.

### Value

Vector with imputed data, same type as `y`, and of length `sum(wy)`

Numeric vector of length `sum(!ry)` with imputations

### Author(s)

Lisa Doove, Stef van Buuren, Elise Dusseldorp, 2012

### References

Doove, L.L., van Buuren, S., Dusseldorp, E. (2014), Recursive partitioning for missing data imputation in the presence of interaction Effects. Computational Statistics \& Data Analysis, 72, 92-104.

Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984), Classification and regression trees, Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.

Van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.

### See Also

`mice`, `mice.impute.rf`, `rpart`, `rpart.control`

Other univariate imputation functions: `mice.impute.lda()`, `mice.impute.logreg.boot()`, `mice.impute.logreg()`, `mice.impute.mean()`, `mice.impute.midastouch()`, `mice.impute.mnar.logreg()`, `mice.impute.norm.boot()`, `mice.impute.norm.nob()`, `mice.impute.norm.predict()`, `mice.impute.norm()`, `mice.impute.pmm()`, `mice.impute.polr()`, `mice.impute.polyreg()`, `mice.impute.quadratic()`, `mice.impute.rf()`, `mice.impute.ri()`

### Examples

    require(rpart)

    imp <- mice(nhanes2, meth = "cart", minbucket = 4)
    plot(imp)

[Package mice version 3.12.0 Index]