| sm.density {sm} | R Documentation |
This function creates a density estimate from data in one, two or three dimensions. In one dimension, an interactive panel can be activated to control the smoothing parameter, in two dimensions a variety of graphical displays can be selected, and in three dimensions a contour surface can be plotted. A number of other features of the construction of the estimate, and of its display, can be controlled.
sm.density(x, h, model = "none", weights = NA, group=NA, ...)
x |
a vector, or a matrix with two or three columns, containing the data. |
h |
a vector of length one, two or three, defining the smoothing parameter.
A normal kernel function is used and h is its standard deviation.
If this parameter is omitted, a normal optimal smoothing parameter is used.
|
model |
This argument applies only with one-dimensional data. Its default value
is "none". If it is set to "Normal" (or indeed any value
other than "none") then a reference band, indicating where a
density estimate is likely to lie when the data are normally
distributed, will be superimposed on any plot.
|
weights |
a vector of integers representing frequencies of individual observations.
Use of this parameter is incompatible with binning; hence nbins must
then be set to 0 or left at its default value NA.
This argument does not apply with three-dimensional data.
|
group |
a vector of groups indicators (numeric or character values) or a factor |
... |
other optional parameters are passed to the sm.options function,
through a mechanism which limits their effect only to this call of the
function. Those specifically relevant for this function are the following:
hmult,
h.weights,
band,
add,
lty,
display,
props,
xlab,
ylab,
zlab,
xlim,
ylim,
yht,
nbins,
ngrid,
eval.points,
panel,
positive,
delta,
theta,
phi;
see the documentation of sm.options for their description.
|
see Chapters 1, 2 and 6 of the reference below. The code for drawing contours of a three-dimensional density estimate has been amended from an original function written by Stuart Young.
a list containing the values of the density estimate at the evaluation points,
the standard error of the estimate, the upper and lower ends of a variability
band, the smoothing parameter, the smoothing parameter weights and the kernel
weights. Less information is supplied when the smoothing parameter weights
or kernel weights are not all 1, or when positive is set to TRUE.
In the three-dimensional case, no information about the estimate is returned.
a plot on the current graphical device is produced, unless the option
display="none" is set.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
hnorm, hsj, hcv,
nise, nmise, sm,
sm.sphere, sm.regression,
sm.options
# A one-dimensional example y <- rnorm(50) sm.density(y, model = "Normal") # A two-dimensional example y <- cbind(rnorm(50), rnorm(50)) sm.density(y, display = "image") # A three-dimensional example y <- cbind(rnorm(50), rnorm(50), rnorm(50)) sm.density(y)