actualPosition: Getting a Sample from Marginal Density Functions
In MaskJointDensity: Masking, Unmasking and Restoring Confidential Data

Description Usage Arguments Details Value Author(s) References Examples

Obtains samples from the marginal density functions of the unmasked variables.

1	actualPosition(vectorL, prob, boundaryVec, size = 1)

`vectorL`	Should be the dimension of the Joint Density Function
`prob`	The Joint Density Function
`boundaryVec`	Boundary of each element, min, max, min, max
`size`	The size of the sample

Used by getSampleFromMarginalDistributionOfUnmaskedData

An n*k matrix where n is the sample size and k is the number of vectors. Each column represents the sample from the marginal density of the kth variable.

Jordan Morris

no references

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (vectorL, prob, boundaryVec, size = 1) 
{
    len <- length(vectorL)
    n <- c()
    for (i in 1:len) {
        n[i] <- vectorL[i]
    }
    maxSize = 1
    for (i in 1:len) {
        maxSize <- maxSize * n[i]
    }
    w <- c(0:(maxSize - 1))
    k <- sample(w, size = size, replace = TRUE, prob = prob)
    barredPoints <- matrix(nrow = size, ncol = len)
    for (i in 1:size) {
        maxSize <- 1
        for (l in 1:len) {
            maxSize <- maxSize * n[l]
        }
        for (j in 1:(len - 1)) {
            maxSize <- maxSize/n[len + 1 - j]
            if (k[i] > maxSize) {
                barredPoints[i, (len + 1 - j)] <- floor(k[i]/maxSize) + 
                  1
                k[i] <- k[i]%%maxSize + 1
            }
            else {
                barredPoints[i, (len + 1 - j)] <- 1
            }
        }
        barredPoints[i, 1] <- k[i] + 1
    }
    return(barredToActual(vectorL, boundaryVec, barredPoints))
  }