binned.kernel.est: Kernel Density Estimation

In philentropy: Similarity and Distance Quantification Between Probability Functions

Kernel Density Estimation

Description

This function implements an interface to the kernel density estimation functions provided by the KernSmooth package.

Usage

binned.kernel.est(
  data,
  kernel = "normal",
  bandwidth = NULL,
  canonical = FALSE,
  scalest = "minim",
  level = 2L,
  gridsize = 401L,
  range.data = range(data),
  truncate = TRUE
)

Arguments

data	a numeric vector containing the sample on which the kernel density estimate is to be constructed.
kernel	character string specifying the smoothing kernel
bandwidth	the kernel bandwidth smoothing parameter.
canonical	a logical value indicating whether canonically scaled kernels should be used
scalest	estimate of scale. "stdev" - standard deviation is used. "iqr" - inter-quartile range divided by 1.349 is used. "minim" - minimum of "stdev" and "iqr" is used.
level	number of levels of functional estimation used in the plug-in rule.
gridsize	the number of equally-spaced points over which binning is performed to obtain kernel functional approximation.
range.data	vector containing the minimum and maximum values of data at which to compute the estimate. The default is the minimum and maximum data values.
truncate	logical value indicating whether data with x values outside the range specified by range.data should be ignored.

Author(s)

Hajk-Georg Drost

References

Matt Wand (2015). KernSmooth: Functions for Kernel Smoothing Supporting Wand & Jones (1995). R package version 2.23-14.

Henry Deng and Hadley Wickham (2011). Density estimation in R. http://vita.had.co.nz/papers/density-estimation.pdf.

philentropy documentation built on May 29, 2024, 1:11 a.m.