#' 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)
}
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