vimpute: Impute missing values with prefered Model, sequentially, with...

In VIM: Visualization and Imputation of Missing Values

Impute missing values with prefered Model, sequentially, with hyperparametertuning and with PMM (if wanted) Need of 'helper_vimpute' script

Description

Impute missing values with prefered Model, sequentially, with hyperparametertuning and with PMM (if wanted) Need of 'helper_vimpute' script

Usage

vimpute(
  data,
  considered_variables = names(data),
  method = setNames(as.list(rep("ranger", length(considered_variables))),
    considered_variables),
  pmm = setNames(as.list(rep(TRUE, length(considered_variables))), considered_variables),
  formula = FALSE,
  sequential = TRUE,
  nseq = 10,
  eps = 0.005,
  imp_var = TRUE,
  pred_history = FALSE,
  tune = FALSE,
  verbose = FALSE
)

Arguments

data	Dataset with missing values. Can be provided as a data.table or data.frame.
considered_variables	A character vector of variable names to be either imputed or used as predictors, excluding irrelevant columns from the imputation process.
method	A named list specifying the imputation method for each variable:
pmm	TRUE/FALSE indicating whether predictive mean matching is used. Provide as a list for each variable.
formula	If not all variables are used as predictors, or if transformations or interactions are required (applies to all X, for Y only transformations are possible). Only applicable for the methods "robust" and "regularized". Provide as a list for each variable that requires specific conditions.
sequential	If TRUE, all variables are imputed sequentially.
nseq	Maximum number of iterations (if sequential is TRUE).
eps	Threshold for convergence.
imp_var	If TRUE, the imputed values are stored.
pred_history	If TRUE, all predicted values across all iterations are stored.
tune	Tunes hyperparameters halfway through iterations, TRUE or FALSE.
verbose	If TRUE additional debugging output is provided

Value

imputed data set or c(imputed data set, prediction history)

See Also

Other imputation methods: hotdeck(), impPCA(), irmi(), kNN(), matchImpute(), medianSamp(), rangerImpute(), regressionImp(), sampleCat(), xgboostImpute()

Examples

## Not run: 
x <- vimpute(data = sleep, sequential = FALSE)
y <- vimpute(data = sleep, sequential = TRUE, nseq = 3)
z <- vimpute(data = sleep, considered_variables =
       c("Sleep", "Dream", "Span", "BodyWgt"), sequential = FALSE)

## End(Not run)

VIM documentation built on Jan. 10, 2026, 9:13 a.m.