#'
#' @title Partial Computation for Model Fitted Values
#' @description Calculates the fitted values in each data node.
#' @details Considering \code{y} as a response variable and x as study variable, the fitted values are the y-values that
#' would expect for the given x-values according to the best-fitting straight line.
#'
#' @param beta is a list of the regression coefficients.
#' @param formula a string character to be transformed as an object of class \code{formula}.
#'
#' @return a vector of fitted values.
#'
#' @section Dependencies:
#' \code{\link{getVarbyFormula}}
#'
#' @author Paula R. Costa e Silva
#' @export
#'
getXValuesComplete <- function(formula, idValuesList) {
#Format variables
vars <- all.vars(formula)
vars <- vars[-1]
xColNames <- vars[-1]
yColNames <- vars[1]
#Data transformations
idValues <- as.numeric(unlist(strsplit(idValuesList, split="x")))
#beta.reg <- data.matrix(beta.reg.aux)
#bindxy <- eval(parse(text="D"))
bindxy <- dataset[,vars]
row.sums <- rowSums(is.na(bindxy))
naLines <- names(which(row.sums!=0))
#Select subset of complete data
xValuesComplete <- bindxy[-which(rownames(bindxy) %in% naLines), ]
imputedValues <- NULL
if(length(idValues) < nrow(xValuesComplete)) {
imputedValues <- xValuesComplete[which(rownames(xValuesComplete) %in% idValues),]
} else if (length(idValues) > nrow(xValuesComplete)) {
imputedValues <- xValuesComplete[which(idValues %in% rownames(xValuesComplete)),]
}
imputedValuesDF <- as.data.frame(imputedValues[,1])
rownames(imputedValuesDF) <- rownames(imputedValues)
colnames(imputedValuesDF) <- "imputedValues"
return(imputedValuesDF)
}
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