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#' Imputation Impact IMI
#'
#' The aggregated imputation impact for variable groups allows measuring the effect of imputations on the distribution of categoric variables (exclusive response groups).
#' @author Beat Hulliger - Juan Berdugo
#' @param data (mandatory): A dataframe containing the data to be processed.
#' @param bij (optional): A matrix containing the structurally missingness indicators. bij can be calculated using the function \code{\link[sdap]{smind}}. If the argument bij is missing, the indicator is calculated without considering a missingness indicators matrix.
#' @param gij (mandatory): A matrix containing the imputation indicators for a given dataframe. gij can be calculated using the function \code{\link[sdap]{impind}}.
#' @param obsi (optional): A vector with the observations in rij to to be processed. If the argument is missing, all observations are processed.
#' @param varj (optional): A vector with the variables (column numbers) to be considered for the calculation. If the argument varj is missing, all variables are considered for the indicator.
#' @param weight (optional): A vector of weights to be considered when calculating the indicator. Default weight is 1.
#' @return A list with the following elements: variables (variables), observations (observations), Number of imputations detected (imputations), Indicator IMI (imi).
#' @export
imi <- function(data,bij,gij,obsi=1:nrow(gij),varj=1:ncol(gij),weight)
{
#obsi <- 1:nrow(gij)
#varj <- var.sie
#weight <- weight.rent
n <- length(obsi)
p <- length(varj)
if (missing(weight)) weight <- rep(1,n)
#Check existence of gij
if (missing(gij)) {
cat("Missing gij!\n")
break
}
#Check existence of bij
if (missing(bij))
{
cat("Missing bij!\n")
break
}
#store the size of r1ij, bij and gij
sizedata <- as.double(dim(data))
sizebij <- as.double(dim(bij))
sizegij <- as.double(dim(gij))
#check if the sizes of bij and gij match
sizeindicators<- (sizebij+sizegij)/2
#check if the sizes of r1ij, bij and gij match
if (!identical(sizeindicators, sizedata))
{
print("The sizes of the datasets do not match. Please recalculate bij and/or gij and/or r1ij.")
break
}else
{
print("Datasets sizes ok")
}
yij <- as.matrix(data[obsi,varj])
class(yij) <- "numeric"
yij[is.na(yij)] <- 0
# Calculate denominator of the function. If it is zero, return zero and break.
denominator <- weight*(1-bij[obsi,varj])*yij
denominator <- colSums(denominator)
numerator <- (weight*(1-bij[obsi,varj])*(gij[obsi,varj])*yij)
numerator <- colSums(numerator)
numerator <- numerator * numerator
imi.value <- numerator / denominator
imi.value <- sum(imi.value) ^(1/2)
imi.value <- imi.value / (sum(weight)*p)^(1/2)
imi.value<- list(variables = varj, observations = obsi, imi=imi.value )
return(imi.value)
}
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