autotune_missRanger: Perform imputation using missRenger form missRegnger package.

In NADIA: NA Data Imputation Algorithms

Perform imputation using missRenger form missRegnger package.

Description

Function use missRenger package for data imputation. Function use OBBerror (more in missForest documentation) to perform random search.

Usage

autotune_missRanger(
  df,
  percent_of_missing = NULL,
  maxiter = 10,
  random.seed = 123,
  mtry = NULL,
  num.trees = 500,
  verbose = FALSE,
  col_0_1 = FALSE,
  out_file = NULL,
  pmm.k = 5,
  optimize = TRUE,
  iter = 10
)

Arguments

df	data.frame. Df to impute with column names and without target column.
percent_of_missing	numeric vector. Vector contatining percent of missing data in columns for example c(0,1,0,0,11.3,..)
maxiter	maximum number of iteration for missRanger algorithm
random.seed	random seed use in imputation
mtry	sample fraction use by missRanger. This param isn't optimized automatically. If NULL default value from ranger package will be used.
num.trees	number of trees. If optimize == TRUE. Param set seq(10,num.trees,iter) will be used.
verbose	If FALSE function doesn't print on console.
col_0_1	decide if add bonus column informing where imputation been done. 0 - value was in dataset, 1 - value was imputed. Default False.
out_file	Output log file location if file already exists log message will be added. If NULL no log will be produced.
pmm.k	Number of candidate non-missing values to sample from in the predictive meanmatching step. 0 to avoid this step. If optimize == TRUE param set sample(1:pmm.k,iter) will be used. If pmm.k==0 missRanger == missForest.
optimize	If TRUE inside optimization will be performed.
iter	Number of iteration for a random search.

Value

Return data.frame with imputed values.

Author(s)

Michael Mayer (2019).

References

Michael Mayer (2019). missRanger: Fast Imputation of Missing Values. R package version 2.1.0. https://CRAN.R-project.org/package=missRanger

Examples

  raw_data <- data.frame(
    a = as.factor(sample(c("red", "yellow", "blue", NA), 1000, replace = TRUE)),
    b = as.integer(1:1000),
    c = as.factor(sample(c("YES", "NO", NA), 1000, replace = TRUE)),
    d = runif(1000, 1, 10),
    e = as.factor(sample(c("YES", "NO"), 1000, replace = TRUE)),
    f = as.factor(sample(c("male", "female", "trans", "other", NA), 1000, replace = TRUE)))

  # Prepering col_type
  col_type <- c("factor", "integer", "factor", "numeric", "factor", "factor")

  percent_of_missing <- 1:6
  for (i in percent_of_missing) {
    percent_of_missing[i] <- 100 * (sum(is.na(raw_data[, i])) / nrow(raw_data))
  }


  imp_data <- autotune_missRanger(raw_data[1:100,], percent_of_missing, optimize = FALSE)

  # Check if all missing value was imputed
  sum(is.na(imp_data)) == 0
  # TRUE

NADIA documentation built on Oct. 3, 2022, 1:05 a.m.