R: Imputation by random forests

mice.impute.rf {mice} | R Documentation

## Imputation by random forests

### Description

Imputes univariate missing data using random forests.

### Usage

    mice.impute.rf(y, ry, x, wy = NULL, ntree = 10, ...)

### 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.
`ntree` | The number of trees to grow. The default is 10.
`...` | Other named arguments passed down to `mice:::install.on.demand()`, `randomForest::randomForest()` and `randomForest:::randomForest.default()`.

### Details

Imputation of `y` by random forests. The method calls `randomForrest()` which implements Breiman's random forest algorithm (based on Breiman and Cutler's original Fortran code) for classification and regression. See Appendix A.1 of Doove et al. (2014) for the definition of the algorithm used.

### Value

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

### Note

An alternative implementation was independently developed by Shah et al (2014). This were available as functions `CALIBERrfimpute::mice.impute.rfcat` and `CALIBERrfimpute::mice.impute.rfcont` (now archived). Simulations by Shah (Feb 13, 2014) suggested that the quality of the imputation for 10 and 100 trees was identical, so mice 2.22 changed the default number of trees from `ntree = 100` to `ntree = 10`.

### 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.

Shah, A.D., Bartlett, J.W., Carpenter, J., Nicholas, O., Hemingway, H. (2014), Comparison of random forest and parametric imputation models for imputing missing data using MICE: A CALIBER study. American Journal of Epidemiology, doi: 10.1093/aje/kwt312.

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

### See Also

`mice`, `mice.impute.cart`, `randomForest`

Other univariate imputation functions: `mice.impute.cart()`, `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.ri()`

### Examples

    library("lattice")

    imp <- mice(nhanes2, meth = "rf", ntree = 3)
    plot(imp)

[Package mice version 3.12.0 Index]