Return a function performing kernel density estimation.
The difference between density
and
densityfun
is similar to that between
approx
and approxfun
.
densityfun(
x,
bw = "nrd0",
adjust = 1,
kernel = "gaussian",
weights = NULL,
window = kernel,
width,
n = 512,
from,
to,
cut = 3,
na.rm = FALSE,
...
)
numeric. The data from which the estimate is to be computed.
numeric. The smoothing bandwidth to be used.
See the eponymous argument of density
.
numeric. The bandwidth used is actually adjust*bw
.
This makes it easy to specify values like 'half the default' bandwidth.
character. A string giving the smoothing kernel to be used.
Authorized kernels are listed in .kernelsList()
.
See also the eponymous argument of density
.
numeric. A vector of non-negative observation weights,
hence of same length as x
.
See the eponymous argument of density
.
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.
The number of equally spaced points at which the density
is to be estimated.
See the eponymous argument of density
.
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.
logical. If TRUE
, missing values are removed
from x
.
If FALSE
any missing values cause an error.
Additional arguments for (non-default) methods.
A function that can be called to generate a density.
# NOT RUN {
x <- rlnorm(1000, 1, 1)
f <- densityfun(x, from = 0)
curve(f(x), xlim = c(0, 20))
# }
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