densityFolded: Folded kernel density estimation

In mcmcOutput: Functions to Store, Manipulate and Display Markov Chain Monte Carlo (MCMC) Output

Folded kernel density estimation

Description

Parameters are often constrained to be greater than zero (eg, standard deviation) or within the range (0, 1) (eg, probabilities), but the function density often returns non-zero densities outside these ranges. Simple truncation does not work, as the area under the curve is < 1. The function densityFolded attempts to identify these constraints and gives an appropriate density.

If x is a matrix, detection of constraints and selection of bandwidth is applied to the pooled values, but a separate density curve is fitted to each column of the matrix.

Usage

densityFolded(x, bw = "nrd0", adjust = 1, from=NA, to=NA, ...)

Arguments

x	a numeric vector or matrix from which the estimate is to be computed; missing values not allowed.
bw	the smoothing bandwidth to be used; see density for details.
adjust	the bandwidth used is actually adjust*bw.
from, to	the lower and upper ends of the grid at which the density is to be estimated; if NA, range will cover the values in x; ignored and replaced with 0 or 1 if a constraint is detected.
...	other arguments passed to density.

Value

Returns a list containing the following components:

x

the n coordinates of the points where the density is estimated.

y

a vector or matrix with the estimated density values.

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.

If y is a vector, the output will have class density.

Author(s)

Mike Meredith

Examples

require(graphics)
oldpar <- par(mfrow=2:1)

x1 <- rnorm(1e4)            # no constraint on x1
plot(density(x1))
plot(densityFolded(x1))     # no difference

x2 <- abs(rnorm(1e4))       # x2 >= 0, with mode at 0
plot(density(x2))           # density > 0 when x2 < 0, mode around 0.2
abline(v=0, col='grey')
plot(densityFolded(x2))     # mode plotted correctly
abline(v=0, col='grey')

x3 <- rbeta(1e4, 1.5, 1.5)  # 0 <= x3 <= 1
plot(density(x3))           # density > 0 when x2 < 0 and x2 > 1
abline(v=0:1, col='grey')
plot(densityFolded(x3))
abline(v=0:1, col='grey')

x4 <- rbeta(1e4, 1.5, 0.9)  # 0 <= x4 <= 1, with mode at 1
plot(density(x4))           # mode appears to be around 0.95
abline(v=0:1, col='grey')
plot(densityFolded(x4))     # mode plotted correctly
abline(v=0:1, col='grey')

# Try with a matrix
x5 <- cbind(rbeta(1e4, 2,2), rbeta(1e4, 2,3), rbeta(1e4, 3,2))
plot(density(x5))
tmp <- densityFolded(x5)
with(tmp, matplot(x, y, type='l'))

par(oldpar)

mcmcOutput documentation built on Nov. 18, 2022, 1:08 a.m.