missMDA_FMAD_MCA_PCA: Perform imputation using MCA, PCA, or FMAD algorithm.

In NADIA: NA Data Imputation Algorithms

Perform imputation using MCA, PCA, or FMAD algorithm.

Description

Function use missMDA package to perform data imputation. Function can found the best number of dimensions for this imputation. User can choose whether to return one imputed dataset or list or imputed datasets form Multiple Imputation.

Usage

missMDA_FMAD_MCA_PCA(
  df,
  col_type = NULL,
  percent_of_missing = NULL,
  optimize_ncp = TRUE,
  set_ncp = 2,
  col_0_1 = FALSE,
  ncp.max = 5,
  return_one = TRUE,
  random.seed = 123,
  maxiter = 998,
  coeff.ridge = 1,
  threshold = 1e-06,
  method = "Regularized",
  out_file = NULL,
  return_ncp = FALSE
)

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,..)
optimize_ncp	logical. If true number of dimensions used to predict the missing entries will be optimized. If False by default ncp = 2 it's used.
set_ncp	intiger >0. Number of dimensions used by algortims. Used only if optimize_ncp = Flase.
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).
ncp.max	integer corresponding to the maximum number of components to test. Default 5.
return_one	One or many imputed sets will be returned. Default True.
random.seed	integer, by default random.seed = NULL implies that missing values are initially imputed by the mean of each variable. Other values leads to a random initialization
maxiter	maximal number of iteration in algortihm.
coeff.ridge	Value use in Regularized method.
threshold	threshold for convergence.
method	method used in imputation algoritm.
out_file	Output log file location if file already exists log message will be added. If NULL no log will be produced.
return_ncp	Function should return used ncp value

Details

Function use different algorithm to adjust for variable types in df. For only numeric data PCA will be used. MCA for only categorical and FMAD for mixed. If optimize==TRUE function will try to find optimal ncp if its not possible default ncp=2 will be used. In some cases ncp=1 will be used if ncp=2 don't work. For multiple imputations, if set ncp don't work error will be return.

Value

Retrun one imputed data.frame if retrun_one=True or list of imputed data.frames if retrun_one=False.

Author(s)

Julie Josse, Francois Husson (2016) doi: 10.18637/jss.v070.i01

References

Julie Josse, Francois Husson (2016). missMDA: A Package for Handling Missing Values in Multivariate Data Analysis. Journal of Statistical Software, 70(1), 1-31. doi:10.18637/jss.v070.i01

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 <- missMDA_FMAD_MCA_PCA(raw_data, col_type, percent_of_missing, optimize_ncp = 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.