R/Internal.R

In JacksonDowns/Lab1Intro: This package is used to complete the lab 1 for Dr. Stewart's MATH 4793

#' Covariance
#'
#' @param vectorx 1st vector parameter
#' @param vectory 2nd vector parameter
#'
#' @return returns the covariance of the two vectors
#'
#'
#'
Covariance = function(vectorx, vectory){

  #top part of the covariance function
  sumOfDifferences = 0

  #Mean value of the first vector parameter
  xbar = VectorMean(vectorx)

  #Mean values of the second vector parameter
  ybar = VectorMean(vectory)

  #length of the two vectors
  n = length(vectorx)

  #Sums the differences of the vector values - their respective means and multiplies the results
  for(i in 1:n) {

    sumOfDifferences = sumOfDifferences + ((vectorx[i] - xbar) * (vectory[i] - ybar))

  }

  #Calculates the covariance
  covar = sumOfDifferences / (n-1)

  #Returns the covariance of the vectors
  return(covar)

}



#' Correlation
#'
#' @param vectorx 1st vector parameter
#' @param vectory 2nd vector parameter
#'
#' @return returns the correlation of the two vectors
#'
#'
#'
Correlation = function(vectorx, vectory){

  numOfDataPoints = length(vectorx)

  devScoreSquaredx = vector()
  devScoreSquaredy = vector()
  crossProducts = vector()

  for (i in 1:numOfDataPoints) {

    devScoreSquaredx = c(devScoreSquaredx,(vectorx[i]-VectorMean(vectorx))^2)
    devScoreSquaredy = c(devScoreSquaredy,(vectory[i]-VectorMean(vectory))^2)
    crossProducts = c(crossProducts,(vectorx[i]-VectorMean(vectorx))*(vectory[i]-VectorMean(vectory)))

  }

  sumDevSCoresx = sum(devScoreSquaredx)
  sumDevScoresy = sum(devScoreSquaredy)
  sumCrossProduct = sum(crossProducts)


  correlation = sumCrossProduct / (sqrt(sumDevSCoresx)*sqrt(sumDevScoresy))

  return(correlation)

}