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philentropy

binned.kernel.est

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

Similarity and Distance Quantification Between Probability Functions

v0.4.0
GPL-2
Authors
Hajk-Georg Drost [aut, cre] (<https://orcid.org/0000-0002-1567-306X>)
Initial release
2020-01-09

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