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, …)
# S3 method for default
density(x, bw = "nrd0", adjust = 1,
kernel = c("gaussian", "epanechnikov", "rectangular",
"triangular", "biweight",
"cosine", "optcosine"),
weights = NULL, window = kernel, width,
give.Rkern = FALSE,
n = 512, from, to, cut = 3, na.rm = FALSE, …)
```

x

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

bw

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, and from S-PLUS.)

`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`

.

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.

weights

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[]`

.

width

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.

give.Rkern

logical; if true, *no* density is estimated, and
the ‘canonical bandwidth’ of the chosen `kernel`

is returned
instead.

n

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)`

.

cut

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.

na.rm

logical; if `TRUE`

, missing values are removed
from `x`

. If `FALSE`

any missing values cause an error.

…

further arguments for (non-default) methods.

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.

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)`

.

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.
http://www.jstor.org/stable/2345597.

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.

# NOT RUN { require(graphics) plot(density(c(-20, rep(0,98), 20)), xlim = c(-4, 4)) # IQR = 0 # The Old Faithful geyser data d <- density(faithful$eruptions, bw = "sj") d plot(d) plot(d, type = "n") polygon(d, col = "wheat") ## Missing values: x <- xx <- faithful$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(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 x.new <- rnorm(N, sample(xx, size = N, replace = TRUE), fit$bw) plot(fit) lines(density(x.new), col = "blue") (kernels <- eval(formals(density.default)$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 ------------- # } # NOT RUN { <!-- %% i.e. "secondary example" in a new help system ... --> # } # NOT RUN { (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) # }

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