VIM (version 7.0.0)

vimpute: 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
)

Value

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

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

See Also

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

Examples

if (FALSE) {
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)
}