density {stats}R Documentation

Kernel Density Estimation


The (S3) generic function density computes kernel density estimates. Its default method does so with the given kernel and bandwidth for univariate observations.


density(x, ...)
## Default S3 method:
density(x, bw = "nrd0", adjust = 1,
        kernel = c("gaussian", "epanechnikov", "rectangular",
                   "triangular", "biweight",
                   "cosine", "optcosine"),
        weights = NULL, window = kernel, width,
        give.Rkern = FALSE, subdensity = FALSE,
        warnWbw = var(weights) > 0,
        n = 512, from, to, cut = 3, ext = 4,
        old.coords = FALSE,
        na.rm = FALSE, ...)



the data from which the estimate is to be computed. For the default method a numeric vector: long vectors are not supported.


the smoothing bandwidth to be used. The kernels are scaled such that this is the standard deviation of the smoothing kernel. (Note this differs from the reference books cited below.)

bw can also be a character string giving a rule to choose the bandwidth. See bw.nrd.
The default, "nrd0", has remained the default for historical and compatibility reasons, rather than as a general recommendation, where e.g., "SJ" would rather fit, see also Venables and Ripley (2002).

The specified (or computed) value of bw is multiplied by 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 partially match 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. However, "cosine" is the version used by S.


numeric vector of non-negative observation weights, hence of same length as x. The default NULL is equivalent to weights = rep(1/nx, nx) where nx is the length of (the finite entries of) x[]. If na.rm = TRUE and there are NA's in x, they and the corresponding weights are removed before computations. In that case, when the original weights have summed to one, they are re-scaled to keep doing so.

Note that weights are not taken into account for automatic bandwidth rules, i.e., when bw is a string. When the weights are proportional to true counts cn, density(x = rep(x, cn)) may be used instead of weights.


this exists for compatibility with S; if given, and bw is not, will set bw to width if this is a character string, or to a kernel-dependent multiple of width if this is numeric.


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


used only when weights are specified which do not sum to one. When true, it indicates that a “sub-density” is desired and no warning should be signalled. By default, when false, a warning is signalled when the weights do not sum to one.


logical, used only when weights are specified and bw is character, i.e., automatic bandwidth selection is chosen (as by default). When true (as by default), a warning is signalled to alert the user that automatic bandwidth selection will not take the weights into account and hence may be suboptimal.


the number of equally spaced points at which the density is to be estimated. When n > 512, it is rounded up to a power of 2 during the calculations (as fft is used) and the final result is interpolated by approx. So it almost always makes sense to specify n as a power of two.

from, to

the left and right-most points of the grid at which the density is to be estimated; the defaults are cut * bw outside of range(x).


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


a positive extension factor, 4 by default. The values from and to are further extended on both sides to lo <- from - ext * bw and up <- to + ext * bw which are then used to build the grid used for the FFT and interpolation, see n above. Do not change unless you know what you are doing!


logical to require pre-R 4.4.0 behaviour which gives too large values by a factor of about (1 + 1/(2n-2)).


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


further arguments for (non-default) methods.


The algorithm used in density.default 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).


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.


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


the estimated density values. These will be non-negative, but can be zero.


the bandwidth used.


the sample size after elimination of missing values.


the call which produced the result.

the deparsed name of the x argument.

logical, for compatibility (always FALSE).

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


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. Journal of the Royal Statistical Society Series B, 53, 683–690. doi:10.1111/j.2517-6161.1991.tb01857.x.

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.

See Also

bw.nrd, plot.density, hist; fft and convolve for the computational short cut used.



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

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

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(0.01, 10))

## Weighted observations:
fe <- sort(faithful$eruptions) # has quite a few non-unique values
## use 'counts / n' as weights:
dw <- density(unique(fe), weights = table(fe)/length(fe), bw = d$bw)
utils::str(dw) ## smaller n: only 126, but identical estimate:
stopifnot(all.equal(d[1:3], dw[1:3]))

## simulation from a density() fit:
# a kernel density fit is an equally-weighted mixture.
fit <- density(xx)
N <- 1e6 <- rnorm(N, sample(xx, size = N, replace = TRUE), fit$bw)
lines(density(, col = "blue")

## The available kernels:
(kernels <- eval(formals(density.default)$kernel))

## show the kernels in the R parametrization
plot (density(0, bw = 1), xlab = "",
      main = "R's density() kernels with bw = 1")
for(i in 2:length(kernels))
   lines(density(0, bw = 1, kernel =  kernels[i]), col = i)
legend(1.5,.4, legend = kernels, col = seq(kernels),
       lty = 1, cex = .8, y.intersp = 1)

## show the kernels in the S parametrization
plot(density(0, from = -1.2, to = 1.2, width = 2, kernel = "gaussian"),
     type = "l", ylim = c(0, 1), xlab = "",
     main = "R's density() kernels with width = 1")
for(i in 2:length(kernels))
   lines(density(0, width = 2, kernel =  kernels[i]), col = i)
legend(0.6, 1.0, legend = kernels, col = seq(kernels), lty = 1)

##-------- Semi-advanced theoretic from here on -------------

## Explore the old.coords TRUE --> FALSE change:
set.seed(7); x <- runif(2^12) # N = 4096
den  <- density(x) # -> grid of n = 512 points
den0 <- density(x, old.coords = TRUE)
summary(den0$y / den$y) # 1.001 ... 1.011
summary(    den0$y / den$y - 1) # ~= 1/(2n-2)
summary(1/ (den0$y / den$y - 1))# ~=    2n-2 = 1022
corr0 <- 1 - 1/(2*512-2) # 1 - 1/(2n-2)
all.equal(den$y, den0$y * corr0)# ~ 0.0001
plot(den$x, (den0$y - den$y)/den$y, type='o', cex=1/4)
title("relative error of density(runif(2^12), old.coords=TRUE)")
abline(h = 1/1022, v = range(x), lty=2); axis(2, at=1/1022, "1/(2n-2)", las=1)

## The R[K] for our kernels:
(RKs <- cbind(sapply(kernels,
                     function(k) density(kernel = k, give.Rkern = TRUE))))
100*round(RKs["epanechnikov",]/RKs, 4) ## Efficiencies

bw <- bw.SJ(precip) ## sensible automatic choice
plot(density(precip, bw = bw),
     main = "same sd bandwidths, 7 different kernels")
for(i in 2:length(kernels))
   lines(density(precip, bw = bw, kernel = kernels[i]), col = i)

## Bandwidth Adjustment for "Exactly Equivalent Kernels"
h.f <- sapply(kernels, function(k)density(kernel = 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, bw = bw),
     main = "equivalent bandwidths, 7 different kernels")
for(i in 2:length(kernels))
   lines(density(precip, bw = bw, adjust = h.f[i], kernel = kernels[i]),
         col = i)
legend(55, 0.035, legend = kernels, col = seq(kernels), lty = 1)

[Package stats version 4.4.1 Index]