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#' @title cortrafo
#'
#' @description
#'
#' An influence function that transforms the response variables (y1, y2, y3...) into scores
#' relevant to the correlations, means, and/or variances.
#'
#' For example, in the case of correlations the variables are transformed into a matrix of
#' (n^2-n)/2 columns (e.g., the number of total correlations), and i rows, where i is the # of
#' observations of y1, where the mean of each vector is equal to the correlation between y1 and y2, y1 and y3, etc.
#'
#' Used internally in when method="ctree".
#'
#' @param data a matrix or data
#' @param weights not currently used
#' @param control not currently used
#' @param model can be any combination of c("correlation", "mean", "variance").
#' Scores are determined based on the specified characteristics
#' @param ... not currently used
#'
#'
#'@export
cortrafo <- function(data, weights, control, model, ...){
data <- as.matrix(data$data[,data$variables$y,drop=FALSE])
obs <- nrow(data)
n <- ncol(data)
function(subset,weights,info,estfun,object,...){
ef <- {
scores <- NULL
if(any("mean" == model)) scores <- cbind(scores, data)
if(any("variance" == model)) scores <- cbind(scores, (data - mean(data))^2)
if(any("correlation" == model)) {
mymat <- matrix(list(), n,n)
for(i in 1:n){
for(j in 1:n){
mymat[[i,j]] <- scale(data[,i]) * scale(data[,j])
}
}
scores <- cbind(scores, matrix(unlist(mymat[lower.tri(mymat)]), obs, (n^2-n)/2))
}
scores
}
list(estfun=ef,
unweighted=TRUE
)
}
}
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