| dsample {dsample} | R Documentation |
Generating Random Samples via Wang-Lee algorithm
Description
dsample generates a sample of specified size n from the target density function (up to a normalizing constant) based on the Wang-Lee algorithm.
Usage
dsample(expr, rpmat, n = 1000, nk = 10000, wconst)
Arguments
expr |
expression of a target density function |
rpmat |
matrix containing random points for discretization |
n |
non-negative integer, the desired sample size. |
nk |
positive integer, the number of contours. See ‘Details’. |
wconst |
real number between 0 and 1. See ‘Details’. |
Details
X has the number of rows equals to the number of discrete base points. In each row, the first element contains the functional value of the target density and the rest elements are the coordinates at which the density is evaluated.
wconst is a constant for adjusting the volume of the last contour.
Value
dsample gives the samples in data.frame with number of rows n and number of columns ncol(rpmat).
References
Wang, L. and Lee, C.H. (2014). Discretization-based direct random sample generation. Computational Statistics and Data Analysis, 71, 1001-1010. Lee, C.H. (2009). Efficient Monte Carlo Random Sample Generation through Discretization, MSc thesis, Department of Satistics, University of Manitoba, Canada
Examples
## Example on page 414 in West (1993)
expr <- expression((x1*(1-x2))^5 * (x2*(1-x1))^3 * (1-x1*(1-x2)-x2*(1-x1))^37)
sets <- list(x1=runif(1e3), x2=runif(1e3))
smp <- dsample(expr=expr, rpmat=sets, nk=1e2, n=1e3)