densityPlot | R Documentation |
densityPlot
contructs and graphs nonparametric density estimates, possibly conditioned on a factor, using the standard R density
function or by default adaptiveKernel
, which computes an adaptive kernel density estimate.
depan
provides the Epanechnikov kernel and dbiwt
provides the biweight kernel.
densityPlot(x, ...)
## Default S3 method:
densityPlot(x, g, method=c("adaptive", "kernel"),
bw=if (method == "adaptive") bw.nrd0 else "SJ", adjust=1,
kernel, xlim, ylim,
normalize=FALSE, xlab=deparse(substitute(x)), ylab="Density", main="",
col=carPalette(), lty=seq_along(col), lwd=2, grid=TRUE,
legend=TRUE, show.bw=FALSE, rug=TRUE, ...)
## S3 method for class 'formula'
densityPlot(formula, data=NULL, subset,
na.action=NULL, xlab, ylab, main="", legend=TRUE, ...)
adaptiveKernel(x, kernel=dnorm, bw=bw.nrd0, adjust=1.0, n=500,
from, to, cut=3, na.rm=TRUE)
depan(x)
dbiwt(x)
x |
a numeric variable, the density of which is estimated; for
|
g |
an optional factor to divide the data. |
formula |
an R model formula, of the form |
data |
an optional data frame containing the data. |
subset |
an optional vector defining a subset of the data. |
na.action |
a function to handle missing values; defaults to the value of the R |
method |
either |
bw |
the geometric mean bandwidth for the adaptive-kernel or bandwidth of the kernel density estimate(s). Must be a numerical value
or a function to compute the bandwidth (default |
adjust |
a multiplicative adjustment factor for the bandwidth; the default, |
kernel |
for |
xlim , ylim |
axis limits; if missing, determined from the range of x-values at which the densities are estimated and the estimated densities. |
normalize |
if |
xlab |
label for the horizontal-axis; defaults to the name of the variable |
ylab |
label for the vertical axis; defaults to |
main |
plot title; default is empty. |
col |
vector of colors for the density estimate(s); defaults to the color |
lty |
vector of line types for the density estimate(s); defaults to the successive integers, starting at 1. |
lwd |
line width for the density estimate(s); defaults to 2. |
grid |
if |
legend |
a list of up to two named elements: |
n |
number of equally spaced points at which the adaptive-kernel estimator is evaluated; the default is |
from , to , cut |
the range over which the density estimate is computed; the default, if missing, is |
na.rm |
remove missing values from |
show.bw |
if |
rug |
if |
... |
arguments to be passed down to graphics functions. |
If you use a different kernel function than the default dnorm
that has a
standard deviation different from 1 along with an automatic rule
like the default function bw.nrd0
, you can attach an attribute to the kernel
function named "scale"
that gives its standard deviation. This is true for
the two supplied kernels, depan
and dbiwt
densityPlot
invisibly returns the "density"
object computed (or list of "density"
objects) and draws a graph.
adaptiveKernel
returns an object of class "density"
(see density)
.
John Fox jfox@mcmaster.ca
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
W. N. Venables and B. D. Ripley (2002) Modern Applied Statistics with S. New York: Springer.
B.W. Silverman (1986) Density Estimation for Statistics and Data Analysis. London: Chapman and Hall.
density
, bw.SJ
, plot.density
densityPlot(~ income, show.bw=TRUE, method="kernel", data=Prestige)
densityPlot(~ income, show.bw=TRUE, data=Prestige)
densityPlot(~ income, from=0, normalize=TRUE, show.bw=TRUE, data=Prestige)
densityPlot(income ~ type, data=Prestige)
densityPlot(~ income, show.bw=TRUE, method="kernel", data=Prestige)
densityPlot(~ income, show.bw=TRUE, data=Prestige)
densityPlot(~ income, from=0, normalize=TRUE, show.bw=TRUE, data=Prestige)
densityPlot(income ~ type, kernel=depan, data=Prestige)
densityPlot(income ~ type, kernel=depan, legend=list(location="top"), data=Prestige)
plot(adaptiveKernel(UN$infantMortality, from=0, adjust=0.75), col="magenta")
lines(density(na.omit(UN$infantMortality), from=0, adjust=0.75), col="blue")
rug(UN$infantMortality, col="cyan")
legend("topright", col=c("magenta", "blue"), lty=1,
legend=c("adaptive kernel", "kernel"), inset=0.02)
plot(adaptiveKernel(UN$infantMortality, from=0, adjust=0.75), col="magenta")
lines(density(na.omit(UN$infantMortality), from=0, adjust=0.75), col="blue")
rug(UN$infantMortality, col="cyan")
legend("topright", col=c("magenta", "blue"), lty=1,
legend=c("adaptive kernel", "kernel"), inset=0.02)
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