estimatePDFmv: Multivariate Nonparametric Density Estimation

In PDFEstimator: Multivariate Nonparametric Probability Density Estimator

Multivariate Nonparametric Density Estimation

Description

Estimates the multivariate probability density function for a data sample containing up to 3 variables.

Usage

estimatePDFmv(sample, debug = 0, resolution = NULL)

Arguments

sample	data sample from which to calculate the density estimate. Each column of data represents an independent variable.
debug	verbose output printed to console
resolution	grid length of data points for each independent variable.

Details

A multivariate nonparametric density estimator based on the maximum-entropy method. Accurately predicts a probability density function (PDF) for random data for 1, 2, or 3 variables.

Value

x	estimated range of density data
pdf	estimated probability density function

Author(s)

Jenny Farmer, Donald Jacobs

References

Farmer, J. and D. Jacobs (2018). "High throughput nonparametric probability density estimation." PLoS One 13(5): e0196937.

Examples

#Estimates a 2-variable normal distribution with 10000 sample points

library(MultiRNG)
nSamples = 5000
cmat = matrix(c(1.0, 0.0, 0.0, 1.0), nrow = 2, ncol = 2)
meanvec = c(0, 0)
sample = draw.d.variate.normal(no.row = nSamples, d = 2,
                               mean.vec = meanvec, cov.mat = cmat)
mvPDF = estimatePDFmv(sample)

PDFEstimator documentation built on Aug. 24, 2023, 9:07 a.m.