autotune_VIM_Irmi: Perform imputation using VIM package and irmi function

In NADIA: NA Data Imputation Algorithms

Perform imputation using VIM package and irmi function

Description

Function use IRMI (Iterative robust model-based imputation ) to impute missing data.

Usage

autotune_VIM_Irmi(
  df,
  col_type = NULL,
  percent_of_missing = NULL,
  eps = 5,
  maxit = 100,
  step = FALSE,
  robust = FALSE,
  init.method = "kNN",
  force = FALSE,
  col_0_1 = FALSE,
  out_file = NULL
)

Arguments

df	data.frame. Df to impute with column names and without target column.
col_type	character vector. Vector containing column type names.
percent_of_missing	numeric vector. Vector contatining percent of missing data in columns for example c(0,1,0,0,11.3,..)
eps	threshold for convergency
maxit	maximum number of iterations
step	stepwise model selection is applied when the parameter is set to TRUE
robust	if TRUE, robust regression methods will be applied (it's impossible to set step=TRUE and robust=TRUE at the same time)
init.method	Method for initialization of missing values (kNN or median)
force	if TRUE, the algorithm tries to find a solution in any case, possible by using different robust methods automatically. (should be set FALSE for simulation)
col_0_1	Decaid if add bonus column informing where imputation been done. 0 - value was in dataset, 1 - value was imputed. Default False. (Works only for returning one dataset).
out_file	Output log file location if file already exists log message will be added. If NULL no log will be produced.

Details

Function can work with various different times depending on data size and structure. In some cases when selected param wouldn't work function try to run on default. Most important param for both quality and reliability its eps.

Value

Return one data.frame with imputed values.

Author(s)

Alexander Kowarik, Matthias Templ (2016) doi: 10.18637/jss.v074.i07

References

Alexander Kowarik, Matthias Templ (2016). Imputation with the R Package VIM. Journal of Statistical Software, 74(7), 1-16. doi:10.18637/jss.v074.i07

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_VIM_Irmi(raw_data, col_type, percent_of_missing)

  # 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.