R/ds.fitted.R

In paularaissa/distStatsClient: Distributed Multiple Imputations

#-------------------------------------- HEADER --------------------------------------------#
#' @title Model Fitted Values
#' @description Computes the fitted values from objects returned by modeling functions.
#' @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 regression an object of regression model.
#' @param checks a boolean, if TRUE (default) checks that verify elements on the server side
#' such checks lengthen the run-time so the default is FALSE and one can switch these checks
#' on (set to TRUE) when faced with some error(s).
#' @param datasources a list of opal object(s) obtained after login in to opal servers;
#' these objects hold also the data assign to R, as \code{data frame}, from opal datasources.
#' @return a list of fitted values.
#' @author Paula Raissa Costa e Silva
#' @section Dependencies:
#' \code{\link{fittedDS}}
#' @export
#' @examples {
#' linear <- ds.linear('D$maternal_age~D$birth_weight')
#' ft <- ds.fitted(linear)
#' }


ds.fitted <- function(regression=NULL, checks=FALSE, datasources=NULL){

  # if no opal login details are provided look for 'opal' objects in the environment
  if(is.null(datasources)){
    datasources <- findLoginObjects()
  }

  #if(!(is.null(regression))) {
    beta.reg <- regression
  #}

  #Get the coefficients
  #if (!(is.null(formula))){
   # beta.reg <- ds.linear(formula)
  #}

  #Data transformations
  beta.vect.temp <- paste0(as.character(beta.reg$coefficients), collapse="x")

  cally <- call('fittedDS', beta.vect.temp, beta.reg$call)
  result <- opal::datashield.aggregate(datasources, cally)

  return(result)

  # yhat <- matrix()
  # for (fitted in result) {
  #   yhat <- rbind(yhat, fitted)
  # }
  #
  # return(yhat)

}