R/ds.impute_bkp.R

In stefvanbuuren/dsMiceClient: Distributed Multiple Imputations

#-------------------------------------- HEADER --------------------------------------------#
#' @title Linear Regression
#' @description Computes linear models. It can be used to fit univariate, multivariate and weighted linear models.
#' It also can be used to compute single stratum analysis of variance and analysis of covariance.
#' @details Models for ds.linear are specified symbolically.
#' A typical model has the form response "~" terms where response is a numeric vector and the terms is a series of terms which
#' specifies a linear predictor for response.
#' A terms specification of the form first + second indicates all the terms in first together with all the terms in second with
#' duplicates removed.
#' A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in
#' first with all terms in second. The specification first*second indicates the cross of first and second.
#'
#' Non-NULL weights can be used to indicate that each independent variable have different variances.
#'
#' In the case of distributed univariate and multivariate linear regression, the coefficients are
#' computed by least squares using the \emph{the method of matrices}.
#' Mathematically, the method of matrices has the same approach than the other methods, but the data is transformed
#' into matrices \emph{A} and \emph{g}.
#'
#' According to \insertCite{walpole1993probability}{distStatsS}, the method of matrices consists in build the
#' matrix \emph{A}, the matrix \emph{g} and calculate the coefficients by the equation \eqn{b={A}^{-1}g}.
#' In distributed environments without data sharing the solution is compute the matrices
#' \emph{A} and \emph{g} for each data node, and return these results to the central node.
#' The central node combines $A$ and $g$, and compute the regression coefficients by the equation \eqn{b={A}^{-1}g}.
#'
#' ds.linear calls the server side function \code{\link{matrixMethod2DS}}, to compute the matrices
#' \emph{A} and \emph{g}.
#' @param formula a character that can be coerced to an object of class \code{\link[stats]{formula}}. It is a symbolic
#' description of the model to be fitted.
#' The details about the model specification are given under 'Details' section.
#' @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 Returns a list with the following components:
#'         \item{call}{the model formula.}
#'         \item{coefficients}{a vector of linear regression coefficients.}
#'         \item{n.rows}{numerical, the sample size.}
#'         \item{sum.y}{numerical, the sum of elements for a given dependent variable \emph{y}.}
#'         \item{sum.xtx}{matrix, the combined A matrix.}
#' @author Paula Raissa Costa e Silva


ds.impute_bkp <- function(coef.table=NULL, coef.frm=NULL, datasources=NULL) {

  if (is.null(datasources))
    datasources <- findLoginObjects()

  beta.reg <- coef.table$Estimate

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

  cally <- call('getFitted', 'betareg')
  result <- opal::datashield.aggregate(datasources, cally)

  return(result)

  # yhat <- matrix()
  # for (fitted in result) {
  #   yhat <- rbind(yhat, fitted)
  # }
  #
  # return(yhat)
  #
  # return(list(coef.table, coef.frm))
  #estimate <- getEstimate(coefficients,datasources) #vector of estimates
}