mice (version 3.4.0)

mice.impute.rf: 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 randomForest() and randomForest:::randomForest.default().

Value

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

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.

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, mice.impute.rfcat, mice.impute.rfcont

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.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

# NOT RUN {
library("lattice")

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

# }