R/ecov_big.R

In Dustin-Wood/funfield: Functional Field Functions

#' Estimate Basic Covariance Elements
#' @description Compute all possible products of effects for all
#' features from weighted ESJT data, \code{wFFdata}.
#'      Note that the average of the \code{dAdB} elements will ultimately
#' equal the covariances forming the expectancy matrix used to
#' estimate functional field models.
#'      Note that this action can be particularly useful when there
#' is a SINGLE action (or TWO MAIN actions) that are being considered for
#' a given scenario (such as 'Cooperate vs. Defect?' in a "Prisoner's Dilemma"
#' situation; or 'Go straight vs. Swerve?' in a "Chicken" situation).  If there are
#' MORE than two actions being represented for a single situation, you may want to
#' use the \code{ecov_long} function instead.
#'
#' Note: data can be weighted using \code{sweighteddata} function
#' @param wFFdata weighted functional field data
#' @return Effect covariance matrix
#' @details
#' Usage notes: FFdata should have format of \code{P_ID}, \code{S_ID}, \code{Did_A}, as first three columns,
#' followed by effect ratings, and end in \code{Likelihood}
#'
#' @export


ecov_big <- function(wFFdata, P_ID = "P_ID", S_ID = "S_ID") {
  #1. Compute effectA*effectB elements.
  data_long <- reshape2::melt(wFFdata, id=c(P_ID,S_ID))
  data_long <- dplyr::rename(data_long, fX=variable, dX=value)
  data_big <-merge(wFFdata,data_long,by.x = c(P_ID,S_ID))
  FF_data_big <- reshape2::melt(data_big, id=c(P_ID,S_ID,"fX","dX"))
  FF_data_big <- dplyr::rename(FF_data_big, fY=variable,dY=value)
  FF_data_big$dXdY<-FF_data_big$dX*FF_data_big$dY

  return(FF_data_big)
}