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Last data update: 2014.03.03

R: Kernel Density Estimation with Reflection

density.reflected

R Documentation

Kernel Density Estimation with Reflection

Description

The function density.reflected computes kernel density estimates for univariate observations using reflection in the borders.

Usage

## S3 method for class 'reflected'
density(x, lower = -Inf, upper = Inf, weights= NULL, ...)

Arguments

`x`	a numeric vector of data from which the estimate is to be computed.
`lower`	the lower limit of the interval to which x is theoretically constrained, default -Inf.
`upper`	the upper limit of the interval to which x is theoretically constrained, default, Inf.
`weights`	numeric vector of non-negative observation weights, hence of same length as x. The default NULL is equivalent to weights = rep(1/length(x), length(x)).
`...`	further `density` arguments.

Details

density.reflected is called by dgeometric.test and computes the density kernel estimate of a univariate random sample x of a random variable defined in the interval (lower,upper) using the default options of density and reflection in the borders. This avoids the density kernel estimate being underestimated in the proximity of lower or upper. For unbounded variables, density.reflected generates the same output as density with its default options.

Value

An object of the class density with borders correction, whose underlying structure is a list containing the following components.

`x`	the `n` coordinates of the points where the density is estimated.
`y`	the estimated density values. These will be non-negative.
`bw`	the bandwidth used.
`n`	the sample size after elimination of missing values.
`call`	the call which produced the result.
`data.name`	the deparsed name of the `x` argument.
`has.na`	logical, for compatibility (always `FALSE`).

The print method reports summary values on the x and y components.

Note

The function is based on density.

Author(s)

Jose M. Pavia

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) "The New S Language." Wadsworth & Brooks/Cole (for S version).

Scott, D. W. (1992) "Multivariate Density Estimation. Theory, Practice and Visualization." New York: Wiley.

Sheather, S. J. and Jones M. C. (1991) "A reliable data-based bandwidth selection method for kernel density estimation." J. Roy. Statist. Soc. B, 683–690.

Silverman, B. W. (1986) "Density Estimation." London: Chapman and Hall.

Venables, W. N. and Ripley, B. D. (2002) "Modern Applied Statistics with S." New York: Springer.

Examples

set.seed(234)
x <- runif(2000)
dx <- density.reflected(x,0,1)

## Plot of the density estimate with and without reflection
par(mfcol=c(1,2))
plot(dx, xlim=c(-0.1,1.1), ylim=c(0,1.1))
abline(h=1, col="red")

plot(density(x), xlim=c(-0.1,1.1), ylim=c(0,1.1))
abline(h=1, col="blue")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(GoFKernel)
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GoFKernel/density.reflected.Rd_%03d_medium.png", width=480, height=480)
> ### Name: density.reflected
> ### Title: Kernel Density Estimation with Reflection
> ### Aliases: density density.reflected
> ### Keywords: density
> 
> ### ** Examples
> 
> set.seed(234)
> x <- runif(2000)
> dx <- density.reflected(x,0,1)
> 
> ## Plot of the density estimate with and without reflection
> par(mfcol=c(1,2))
> plot(dx, xlim=c(-0.1,1.1), ylim=c(0,1.1))
> abline(h=1, col="red")
> 
> plot(density(x), xlim=c(-0.1,1.1), ylim=c(0,1.1))
> abline(h=1, col="blue")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>