R: Kernel Density Estimation

binned.kernel.est {philentropy} | R Documentation

## 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.

[Package philentropy version 0.6.0 Index]