R: Kernel Density Estimation

density {base}

R Documentation

Kernel Density Estimation

Description

The function density computes kernel density estimates with the given kernel and bandwidth.

The generic functions plot and print have methods for density objects.

Usage

density(x, bw, adjust = 1,
        kernel=c("gaussian", "epanechnikov", "rectangular", "triangular",
                 "biweight", "cosine", "optcosine"),
        window = kernel, width,
        give.Rkern = FALSE,
        n = 512, from, to, cut = 3, na.rm = FALSE)
print(dobj)
plot(dobj, main = NULL, xlab = NULL, ylab = "Density", type = "l",
     zero.line = TRUE, ...)

Arguments

x

the data from which the estimate is to be computed.

bw

the smoothing bandwidth to be used. The kernels are scaled such that this is the standard deviation of the smoothing kernel. It defaults to 0.9 times the minimum of the standard deviation and the interquartile range divided by 1.34 times the sample size to the negative one-fifth power (= Silverman's “rule of thumb”) unless the quartiles coincide where bw > 0 will be guaranteed. The specified (or default) value of bw is multiplied by adjust.

adjust

the bandwidth used is actually adjust*bw. This makes it easy to specify values like “half the default” bandwidth.

kernel, window

a character string giving the smoothing kernel to be used. This must be one of "gaussian", "rectangular", "triangular", "epanechnikov", "biweight", "cosine" or "optcosine", with default "gaussian", and may be abbreviated to a unique prefix (single letter).

"cosine" is smoother than "optcosine", which is the usual “cosine” kernel in the literature and almost MSE-efficient.

width

this exists for compatibility with S; if given, and bw is not, will set bw = width/4.

give.Rkern

logical; if true, no density is estimated, and the “canonical bandwidth” of the chosen kernel is returned instead.

n

the number of equally spaced points at which the density is to be estimated. When n > 512, it is rounded up to the next power of 2 for efficiency reasons (fft).

from, to

the left and right-most points of the grid at which the density is to be estimated.

cut

by default, the values of left and right are cut bandwidths beyond the extremes of the data. This allows the estimated density to drop to approximately zero at the extremes.

na.rm

logical; if TRUE, missing values are removed from x. If FALSE any missing values cause an error.

dobj

a “density” object.

main, xlab, ylab, type

plotting parameters with useful defaults.

...

further plotting parameters.

zero.line

logical; if TRUE, add a base line at y = 0

Details

The algorithm used in density disperses the mass of the empirical distribution function over a regular grid of at least 512 points and then uses the fast Fourier transform to convolve this approximation with a discretized version of the kernel and then uses linear approximation to evaluate the density at the specified points.

The statistical properties of a kernel are determined by \sigma^2_K = \int t^2 K(t) dt which is always = 1 for our kernels (and hence the bandwidth bw is the standard deviation of the kernel) and R(K) = \int K^2(t) dt.
MSE-equivalent bandwidths (for different kernels) are proportional to \sigma_K R(K) which is scale invariant and for our kernels equal to R(K). This value is returned when give.Rkern = TRUE. See the examples for using exact equivalent bandwidths.

Infinite values in x are assumed to correspond to a point mass at +/-Inf and the density estimate is of the sub-density on (-Inf, +Inf).

Value

If give.Rkern is true, the number R(K), otherwise an object with class "density" 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.

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).

References

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

Venables, W. N. and B. D. Ripley (1994, 7, 9) Modern Applied Statistics with S-PLUS. New York: Springer.

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.

Examples

plot(density(c(-20,rep(0,98),20)), xlim = c(-4,4))# IQR = 0

# The Old Faithful geyser data
data(faithful)
d <- density(faithful$eruptions, bw = 0.15)
d
plot(d)

plot(d, type = "n")
polygon(d, col = "wheat")

## Missing values:
x <- xx <- faithful$eruptions
x[i.out <- sample(length(x), 10)] <- NA
doR <- density(x, bw = 0.15, na.rm = TRUE)
lines(doR, col = "blue")
points(xx[i.out], rep(.01,10))


(kernels <- eval(formals(density)$kernel))

plot (density(0,bw = 1))
for(i in 2:length(kernels))
   lines(density(0,bw = 1, kern =  kernels[i]), col = i)
mtext(side = 3, "R's density() kernels with bw = 1")
legend(1.5,.4, leg = kernels, col = seq(kernels),lty = 1, cex = .8, y.int = 1)

(RKs <- cbind(sapply(kernels, function(k)density(kern = k, give.Rkern = TRUE))))
100*round(RKs["epanechnikov",]/RKs, 4) ## Efficiencies

data(precip)
plot(density(precip, n = 2^13))
for(i in 2:length(kernels))
   lines(density(precip, kern =  kernels[i], n = 2^13), col = i)
mtext(side = 3, "same scale bandwidths, 7 different kernels")

## Bandwidth Adjustment for "Exactly Equivalent Kernels"
h.f <- sapply(kernels, function(k)density(kern = k, give.Rkern = TRUE))
(h.f <- (h.f["gaussian"] / h.f)^ .2)
## -> 1, 1.01, .995, 1.007,... close to 1 => adjustment barely visible..

plot(density(precip, n = 2^13))
for(i in 2:length(kernels))
   lines(density(precip, adjust = h.f[i], kern =  kernels[i], n = 2^13),
         col = i)
mtext(side = 3, "equivalent bandwidths, 7 different kernels")
legend(55,.035, leg = kernels, col = seq(kernels), lty = 1)

[Package base version 1.1 ]