estimatePDF {PDFEstimator}R Documentation

Nonparametric Density Estimation

Description

Estimates the probability density function for a data sample.

Usage

estimatePDF(sample, pdfLength = NULL, lowerBound = NULL, upperBound = NULL)

Arguments

sample

the data sample from which to calculate the density estimate

pdfLength

the desired length of the estimate returned. Default value is calculated based on sample length. Overriding this calculation can increase or decrease the resolution of the estimate.

lowerBound

the lower bound of the PDF, if known. Default value is calculated based on the range of the data sample.

upperBound

the upper bound of the PDF, if known. Default value is calculated based on the range of the data sample.

Details

A nonparametric density estimator based on the maximum-entropy method. Accurately predicts a probability density function (PDF) for random data using a novel iterative scoring function to determine the best fit without overfitting to the sample.

Value

x

estimated range of density data

pdf

estimated probability density function

cdf

estimated cummulative density function

sqr

scaled quantile residual. Provides a sample-size invariant measure of the fluctuations in the estimate.

lagrange

lagrange multipliers. Can be used to reproduce the expansions for an analytical solution.

failedSolution

returns true if the pdf calculated is not considered an acceptable estimate of the data according to the scoring 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 normal distribution with 100 sample points using default parameters

sampleSize = 1000
sample = rnorm(sampleSize, 0, 1)
dist = estimatePDF(sample)
plot(dist$x, dist$pdf)


[Package PDFEstimator version 0.1-3 Index]