Imputation by random forests

Imputes univariate missing data using random forests.

mice.impute.rf(
  y,
  ry,
  x,
  wy = NULL,
  ntree = 10,
  rfPackage = c("ranger", "randomForest", "literanger"),
  ...
)

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.

rfPackage

A single string specifying the backend for estimating the random forest. The default backend is the ranger package. An alternative is literanger which predicts faster but does not support all forest types and split rules from ranger. Also implemented as an alternative is the randomForest package, which used to be the default in mice 3.13.10 and earlier.

...

Other named arguments passed down to mice:::install.on.demand(), randomForest::randomForest(), randomForest:::randomForest.default(), ranger::ranger(), and literanger::train().

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.

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.

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, ranger, train

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

Author

Lisa Doove, Stef van Buuren, Elise Dusseldorp, 2012; Patrick Rockenschaub, 2021

Examples

if (FALSE) { # \dontrun{
imp <- mice(nhanes2, meth = "rf", ntree = 3)
plot(imp)
} # }