MaskJointDensity (version 1.0)

sampleDensity: A function used to simulate a sample from a kernel function, where the kernel function is determined by a given sample.

Description

Very similar to EQsampleDensity without the restriction of having the nodes equally spaced.

Usage

sampleDensity(sx, boundaryVec, NoNote = 151, size = 100)

Arguments

sx

Matrix where each column corresponds to a vector

boundaryVec

Vector of boundaries of the columns of sx in the order from_1, to_1, from_2, to_2 etc

NoNote

Number of nodes

size

Size of the sample

Value

See actualPosition

References

no references

Examples

# NOT RUN {
##---- 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 (sx, boundaryVec, NoNote = 151, size = 100) 
{
    n <- NoNote
    SXdim <- length(sx[1, ])
    vectorL <- rep(n, SXdim)
    XP <- NULL
    for (i in 1:SXdim) {
        a <- boundaryVec[2 * i - 1]
        b <- boundaryVec[2 * i]
        xposition <- seq(from = a, to = b, by = (b - a)/(n - 
            1))
        XP <- c(XP, xposition)
    }
    NotePositions <- positions(XP, vectorL)
    H <- Hpi(x = sx)
    fhat <- kde(x = sx, H = H)
    prob <- predict(fhat, x = NotePositions)
    outSample <- actualPosition(vectorL, prob, boundaryVec, size)
    return(outSample)
  }
# }