Decision Tree Imputation

Imputation based on CART models or Random Forests.

impute_cart(dat, formula, add_residual = c("none", "observed", "normal"), cp,
  na_action = na.rpart, ...)

impute_rf(dat, formula, add_residual = c("none", "observed", "normal"), na_action = na.omit, ...)

[data.frame], with variables to be imputed and their predictors.
[formula] imputation model description (see Details below).
[character] Type of residual to add. "normal" means that the imputed value is drawn from N(mu,sd) where mu and sd are estimated from the model's residuals (mu should equal zero in most cases). If add_residual = "observed", residuals are drawn (with replacement) from the model's residuals. Ignored for non-numeric predicted variables.
The complexity parameter used to prune the CART model. If omitted, no pruning takes place. If a single number, the same complexity parameter is used for each imputed variable. If of length # of variables imputed, the complexity parameters used must be in the same order as the predicted variables in the model formula.
[function] what to do with missings in training data. By default cases with missing values in predicted or predictors are omitted (see `Missings in training data').
further arguments passed to
Model specification

Formulas are of the form


The left-hand-side of the formula object lists the variable or variables to be imputed. Variables on the right-hand-side are used as predictors in the CART or random forest model.

If grouping variables are specified, the data set is split according to the values of those variables, and model estimation and imputation occur independently for each group.

Grouping using dplyr::group_by is also supported. If groups are defined in both the formula and using dplyr::group_by, the data is grouped by the union of grouping variables. Any missing value in one of the grouping variables results in an error.


CART imputation by impute_cart can be used for numerical, categorical, or mixed data. Missing values are estimated using a Classification and Regression Tree as specified by Breiman, Friedman and Olshen (1984). This means that prediction is fairly robust agains missingess in predictors.

Random Forest imputation with impute_rf can be used for numerical, categorical, or mixed data. Missing values are estimated using a Random Forest model as specified by Breiman (2001).


Breiman, L., Friedman, J., Stone, C.J. and Olshen, R.A., 1984. Classification and regression trees. CRC press.

Breiman, L., 2001. Random forests. Machine learning, 45(1), pp.5-32.

See Also

Other imputation: impute_hotdeck, impute_lm, impute

  • impute_cart
  • impute_rf
Documentation reproduced from package simputation, version 0.2.2, License: GPL-3

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