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mice DRF (Distributional Random Forest)

Source: R/miceDRF.R
mice.impute.DRF.Rd

This function performs imputation using MICE and Distributional Random Forest

Usage

mice.impute.DRF(
  y,
  ry,
  x,
  wy = NULL,
  min.node.size = 1,
  num.features = 10,
  num.trees = 10,
  ...
)

Arguments

y

Vector to be imputed.

ry

Logical vector indicating which elements of `y` are used to fit the imputation model (TRUE = observed).

x

Numeric design matrix with `length(y)` rows, containing predictors for `y` and no missing values.

wy

Logical vector indicating elements of `y` for which imputations are generated.

min.node.size

target minimum number of observations in each tree leaf in DRF. The default value is 5.

num.features

the number of random features to sample.

num.trees

number of trees in DRF. Default to 10.

...

used for compatibility with mice package.

References

This method is described in detail in:

Näf, J., Scornet, E., & Josse, J. (2024). What is a good imputation under MAR missingness?. arXiv. https://arxiv.org/abs/2403.19196

It's based on:

Cevid, D., Michel, L., Näf, J., Meinshausen, N., and B¨ uhlmann, P. (2022). Distributional random forests: Heterogeneity adjustment and multivariate distributional regression. Journal of Machine Learning Research, 23(333):1–79.