approximatePoints: Approximate Data Points

In PDFEstimator: Multivariate Nonparametric Probability Density Estimator

Approximate Data Points

Description

Returns additional point estimates based on an existing estimate.

Usage

approximatePoints(estimate, estimationPoints)

Arguments

estimate	the pdfe object returned from estimatePDF or convertToPDFe
estimationPoints	a vector of additional points to estimate.

Details

This method approximates density estimates for the points specified by performing a linear interpolation on an existing probability density function. For a more precise point estimation, call estimatePDF with the estimationPoints argument.

Value

No return value, called for side effects

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 1000 sample points using default
# parameters, then prints approximate probability density at points -3, 0, and 1
  
sampleSize = 1000
sample = rnorm(sampleSize, 0, 1)
dist = estimatePDF(sample)
approximatePoints(dist, c(-3, 0, 1))

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