autotune_Amelia: Perform imputation using Amelia package and EMB algorithm.
In NADIA: NA Data Imputation Algorithms

autotune_Amelia

R Documentation

Perform imputation using Amelia package and EMB algorithm.

Description

Function use EMB (Expectation-Maximization with Bootstrapping ) to impute missing data. Function performance is highly depend from data structure and chosen parameters.

Usage

autotune_Amelia(
  df,
  col_type = NULL,
  percent_of_missing = NULL,
  col_0_1 = FALSE,
  parallel = TRUE,
  polytime = NULL,
  splinetime = NULL,
  intercs = FALSE,
  empir = NULL,
  verbose = FALSE,
  return_one = TRUE,
  m = 3,
  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,..)
`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).
`parallel`	If true parallel calculation is used.
`polytime`	parameter pass to amelia function
`splinetime`	parameter pass to amelia finction
`intercs`	parameter pass to amleia function
`empir`	parameter pass to amelia function as empir in Amelia == empirnrow(df). If empir dont set empir=nrow(df)0.015.
`verbose`	If true function will print on console.
`return_one`	Decide if one dataset or amelia object will be returned.
`m`	Number of datasets generated by amelia. If retrun_one=TRUE first dataset will be given.
`out_file`	Output log file location if file already exists log message will be added. If NULL no log will be produced.

Value

Return one data.frame with imputed values or amelia object.

Author(s)

James Honaker, Gary King, Matthew Blackwell (2011).

References

James Honaker, Gary King, Matthew Blackwell (2011). Amelia II: A Program for Missing Data. Journal of Statistical Software, 45(7), 1-47. URL https://www.jstatsoft.org/v45/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_Amelia(raw_data, col_type, percent_of_missing,parallel = 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.