missRanger: Fast Imputation of Missing Values by Chained Random Forests

View source: R/missRanger.R

missRangerR Documentation

Fast Imputation of Missing Values by Chained Random Forests


Uses the "ranger" package (Wright & Ziegler) to do fast missing value imputation by chained random forests, see Stekhoven & Buehlmann and Van Buuren & Groothuis-Oudshoorn. Between the iterative model fitting, it offers the option of predictive mean matching. This firstly avoids imputation with values not present in the original data (like a value 0.3334 in a 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This allows to do multiple imputation when repeating the call to missRanger().


  formula = . ~ .,
  pmm.k = 0L,
  maxiter = 10L,
  seed = NULL,
  verbose = 1,
  returnOOB = FALSE,
  case.weights = NULL,
  data_only = TRUE,
  keep_forests = FALSE,



A data.frame with missing values to impute.


A two-sided formula specifying variables to be imputed (left hand side) and variables used to impute (right hand side). Defaults to . ~ ., i.e., use all variables to impute all variables. For instance, if all variables (with missings) should be imputed by all variables except variable "ID", use . ~ . - ID. Note that a "." is evaluated separately for each side of the formula. Further note that variables with missings must appear in the left hand side if they should be used on the right hand side.


Number of candidate non-missing values to sample from in the predictive mean matching steps. 0 to avoid this step.


Maximum number of chaining iterations.


Integer seed to initialize the random generator.


Controls how much info is printed to screen. 0 to print nothing. 1 (default) to print a progress bar per iteration, 2 to print the OOB prediction error per iteration and variable (1 minus R-squared for regression). Furthermore, if verbose is positive, the variables used for imputation are listed as well as the variables to be imputed (in the imputation order). This will be useful to detect if some variables are unexpectedly skipped.


Logical flag. If TRUE, the final average out-of-bag prediction errors per variable is added to the resulting data as attribute "oob". Only relevant when data_only = TRUE (and when forests are grown).


Vector with non-negative case weights.


If TRUE (default), only the imputed data is returned. Otherwise, a "missRanger" object with additional information is returned.


Should the random forests of the final imputations be returned? The default is FALSE. Setting this option will use a lot of memory. Only relevant when data_only = TRUE (and when forests are grown).


Arguments passed to ranger::ranger(). If the data set is large, better use less trees (e.g. num.trees = 20) and/or a low value of sample.fraction. The following arguments are incompatible, amongst others: write.forest, probability, split.select.weights, dependent.variable.name, and classification.


The iterative chaining stops as soon as maxiter is reached or if the average out-of-bag (OOB) prediction errors stop reducing. In the latter case, except for the first iteration, the second last (= best) imputed data is returned.

OOB prediction errors are quantified as 1 - R^2 for numeric variables, and as classification error otherwise. If a variable has been imputed only univariately, the value is 1.

A note on mtry: Be careful when passing a non-default mtry to ranger::ranger() because the number of available covariates might be growing during the first iteration, depending on the missing pattern. Values NULL (default) and 1 are safe choices. Additionally, recent versions of ranger::ranger() allow mtry to be a single-argument function of the number of available covariables, e.g., mtry = function(m) max(1, m %/% 3).


If data_only an imputed data.frame. Otherwise, a "missRanger" object with the following elements that can be extracted via $:

  • data: The imputed data.

  • forests: When keep_forests = TRUE, a list of "ranger" models used to generate the imputed data. NULL otherwise.

  • visit_seq: Variables to be imputed (in this order).

  • impute_by: Variables used for imputation.

  • best_iter: Best iteration.

  • pred_errors: Per-iteration OOB prediction errors (1 - R^2 for regression, classification error otherwise).

  • mean_pred_errors: Per-iteration averages of OOB prediction errors.


  1. Wright, M. N. & Ziegler, A. (2016). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, in press. <arxiv.org/abs/1508.04409>.

  2. Stekhoven, D.J. and Buehlmann, P. (2012). 'MissForest - nonparametric missing value imputation for mixed-type data', Bioinformatics, 28(1) 2012, 112-118. https://doi.org/10.1093/bioinformatics/btr597.

  3. Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. http://www.jstatsoft.org/v45/i03/


irisWithNA <- generateNA(iris, seed = 34)
irisImputed <- missRanger(irisWithNA, pmm.k = 3, num.trees = 100)

## Not run: 
# Extended output
imp <- missRanger(irisWithNA, pmm.k = 3, num.trees = 100, data_only = FALSE)

# If you even want to keep the random forests of the best iteration
imp <- missRanger(
  irisWithNA, pmm.k = 3, num.trees = 100, data_only = FALSE, keep_forests = TRUE
imp$pred_errors[imp$best_iter, "Sepal.Width"]  # 1 - R-squared

## End(Not run)

missRanger documentation built on Nov. 19, 2023, 5:14 p.m.