#' The function for hierarchical imputation of semicontinous variables.
#'
#' The function is called by the wrapper. We consider data to be "semicontinuous" when
#' more than 5\% of the (non categorical) observations.\cr
#' For example in surveys a certain portion of people, when asked for their income,
#' report "0", which clearly violates the assumption of income to be (log-) normally distributed.
#' @param y_imp_multi A Vector with the variable to impute.
#' @param X_imp_multi A data.frame with the fixed effects variables.
#' @param heap A scalar saying to which value the data might be heaped.
#' @param M An integer defining the number of imputations that should be made.
#' @return A n x M matrix. Each column is one of M imputed y-variables.
imp_semicont_single <- function(y_imp_multi,
X_imp_multi,
heap = 0,
M = 10){
tmp_data <- cbind(y_imp_multi, X_imp_multi)
n <- nrow(tmp_data)
#the missing indactor indicates, which values of y are missing.
mis_indicator <- is.na(y_imp_multi)
#get the defaults values for heap
if(is.null(heap)) heap <- 0
# transform y_imp_multi into a binary variable,
# with 0 representing a heaped value and 1 a non heaped value
# NA values will remain NA.
y_binary <- y_imp_multi
# The observations beeing heaped and not NA...
condition_0 <- (y_imp_multi == heap) & !is.na(y_imp_multi)
#... are set to be 0.
y_binary[condition_0] <- 0
# The observations beeing not heaped and not NA...
condition1 <- (y_imp_multi != heap) & !is.na(y_imp_multi)
#... are set to be 1.
y_binary[condition1] <- 1
# Use the imputation function of the binary variable on the indicator
# to figure out whether a missing value shall get the value of the heap or
# a continuous.
# For the data points with an observed y_imp_multi,
# this also indicates whether they are used in the continuous imputation model or not.
what_method <- imp_binary_single(y_imp_multi = y_binary,
X_imp_multi = X_imp_multi)
# set up the results matrix
y_imp <- array(NA, dim = c(n, M))
for(i in 1:M){
# the data points where the binary varriable is "1" (meaning continuous)
# are used for the continuous imputation
y1_imp <- imp_cont_single(y_imp_multi = y_imp_multi[what_method[, i] == 1],
X_imp_multi = X_imp_multi[what_method[, i] == 1, , drop = FALSE])
# set up the final i-th imputation vector
y_tmp <- what_method[, i]
# the data points where the binary imputation said, that they shall be (or already are) continuous,
# get the value of the continuous imputation
y_tmp[what_method[, i] == 1] <- y1_imp
y_imp[ , i] <- y_tmp
}
return(y_imp)
}
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