panel.kernelDensity(x, y, z = NULL, ..., n = 20,
kernel.fun = NULL, panel.range = TRUE,
process = TRUE, plot = TRUE, loa.settings = FALSE)
panel.binPlot(x = NULL, y = NULL, z = NULL,
breaks=20, x.breaks = breaks, y.breaks = breaks,
x1=NULL, x2=NULL, y1=NULL, y2=NULL,
statistic = mean, pad.grid = FALSE, ...,
plot = TRUE, process = TRUE, loa.settings = FALSE)lattice function arguments passed down to
individual panels.panel.kernelDensity, the number of x and y
cases to estimate when estimating density.panel.kernelDensity, a logical (default
FALSE) indicating if the kernel density estimation data range
should be forced to the full panel range. See Belowpanel... functions that
intended to be handled using panelPal. process
and plot, logicals, indicating if the process and plot sections
of the panelpanel.binPlot, how many
break points to introduce when binning data. breaks can be used
to use the same number of breaks on both axes, while x.breaks and
y.breaks can be used to set thespanel.binPlot, vectors giving the
bin cell dimensions used when binning x and y elements.
Typically ignored and calculated within the plot call.panel.binPlot, the function to use when
calculating z values for each set of binned. By default, this is
mean. So, if a z element is supplied in the plot call, the
data is binned accordinpanel.binPlot, Logical, should empty bins be
reported? If TRUE, they are reported as NAs; if FALSE,
they are not reported.panel... functions can be used be used with
conventional lattice functions like xyplot, e.g.:
xyplot(..., panel = panel.kernelDensity)
xyplot(..., n = 50, panel = panel.kernelDensity)
xyplot(..., panel = function(...) panel.kernelDensity(..., n = 50))
#etc}
However, they are intended for use with panelPal.
The combination provides a mechanism for the routine preprocessing of
panel data, the association of specialist keys, and the routine alignment
of panel and legend settings in cases where values are reworked within the
panel function call.
Typically, these are intended for use with panelPal, e.g.:
loaPlot(..., panel = panel.kernelDensity)
#etc}
panel.kernelDensity generates kernel density estimations
based on the supplied x and y data ranges. It ignores
any supplied z information if supplied in the form:
loaPlot(z~x*y, ..., panel = panel.kernelDensity)
panel.binPlot bins supplied z data according to x and
y values and associated break points (set by break arguments),
and then calculates the required statistic for each of these. By default,
this is mean, but alternative functions can be set using the
statistic argument. if no z values are supplied, as in
loaPlot(~x*y, ..., panel = panel.binPlot)
... panel.binPlot resets statistic to length and
gives a count of the number of elements in each bin.
These panel... functions
As with other panel... functions in this package, output are suitable
for use as the panel argument in loa (and sometimes
lattice) plot calls.
These function makes extensive use of code developed by others.
lattice:
Sarkar, Deepayan (2008) Lattice: Multivariate Data
Visualization with R. Springer, New York. ISBN
978-0-387-75968-5
(for panel.kernelDensity) MASS package:
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics
with S. Fourth edition. Springer.
[object Object]
Both panel... functions can color both regions and lines. For
panel.kernelDensity these are colored regions and contour lines
separating them. For panel.binPlot these are the individual data
bins and the borders surrounding them. In both cases the color scheme
applied to the colored regions ares controlled by col.regions and the
colors applied to the lines are controlled by col.
panel.kernelDensity passes additional arguments on to the
kernel.fun to estimate kerenel density and the lattice
function panel.contourplot to generate the associated plot.
If no kernel.fun is supplied in the panel call, the
MASS function kde2d is used to estimate kernel density.
panel.binPlot passes limited arguments on to lrect.
In loa: panelPal
In lattice: xyplot, panel.contourplot,
lrect
## Example 1
## Specialist kernel density panel example
a <- rnorm(1000)
b <- rnorm(1000)
c <- rnorm(1000)
xyplot(a~b, panel = panel.kernelDensity, at = 0:5*5)
loaPlot(~a*b, panel = panel.kernelDensity)
# Note 1:
# at sets col.regions for the color surface, but, as this is calculated
# in-panel, this is not known at time of call. So, you need to set when
# using specialist panels with standard lattice plots.
# (Same is ture for any panel where plot attributes that are set in-panel
# and need to be known in all panels and keys for consistent output.)
# loa panels include separate process and plot steps that panelPal can use
# to track these.
# Note 2:
# By default, the panel ignores z data.
#
# compare:
# loaPlot(c~a*b, panel = panel.kernelDensity) #where z term (c) ignored
# loaPlot(c~a*b, panel = panel.kernelDensity, n=100) #finer surface resolution
## Example 2
## Specialist bin plot panel example
# By default, the panel bins supplied z case as mean
# modify by supplying alternative as statistic
loaPlot(c~a*b, panel = panel.binPlot)
loaPlot(c~a*b, panel = panel.binPlot, statistic=max)
# Note:
# If z is not supplied, statistic defaults to length to give a count
#
# loaPlot(~a*b, panel = panel.binPlot) #where z term not supplied
#etc.
methods