Specialist panel functions for use with lattice and loa plots.
panel.loaLevelPlot(x = NULL, y = NULL, z = NULL, ...,
loa.settings = FALSE)panel.surfaceSmooth(x = NULL, y = NULL, z = NULL,
breaks = 200, x.breaks = breaks, y.breaks = breaks,
smooth.fun = NULL, too.far=0, ...,
plot = TRUE, process = TRUE, loa.settings = FALSE)
panel.kernelDensity(x, y, z = NULL, ..., n = 20,
local.wt = TRUE, kernel.fun = NULL, too.far = 0,
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 = NULL, pad.grid = FALSE, ...,
plot = TRUE, process = TRUE, loa.settings = FALSE)
As with other panel... functions in this package, output are suitable
for use as the panel argument in loa (and sometimes
lattice) plot calls.
lattice function arguments passed down to
individual panels.
Additional arguments, typically passed on. See below.
For panel... functions that
intended to be handled using panelPal. loa.settings,
a logical indicating if the safe mode setting should be returned.
process and plot, logicals, indicating if the process and
plot sections of the panel function should be run. See below and
panelPal help documents for further details.
(For panel.surfaceSmooth and
panel.binPlot) How many break points to introduce when smoothing
a surface or binning data. breaks can be used to set the same number
of breaks on both axes, while x.breaks and y.breaks can be
used to set these separately.
(For panel.surfaceSmooth) A function that can fit
a surface estimate to (x,y,z) data. See notes below for further
details.
(For panel.surfaceSmooth and panel.kernelDensity) The distance from original data
at which to stop predicting surface values. See notes below for further
details.
(For panel.kernelDensity) the number of x and y
cases to estimate when estimating density.
(For panel.kernelDensity) A logical (default TRUE)
indicating if kernel density estimates should be weighed relative to other
groups, panels, etc.
(For panel.kernelDensity) A function that can
estimate kernel densities.
(For panel.kernelDensity) A logical (default
FALSE) indicating if the kernel density estimation data range
should be forced to the full panel range. See Below.
(For panel.binPlot) Vectors giving the
bin cell dimensions used when binning x and y elements.
Typically ignored and calculated within the plot call.
(For 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 according to x and y values, and the mean of
z values within each bin reported/plotted. If z is not
supplied, statistic is reset to length to generate a frequency
plot and a warning generated.
For panel.binPlot, Logical, should empty bins be
reported? If TRUE, they are reported as NAs; if FALSE,
they are not reported.
Karl Ropkins
panel.loaLevelPlot is intended for plot data structured
for use with the lattice function levelplot,
e.g. regularised (x,y,z) or a matrix:
loaPlot(..., panel = panel.loaLevelPlot)
levelplot(...) #in lattice
Other specialist panel... functions can be used with the
lattice function xyplot:
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 loa plots that incorporate
panelPal. This 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:
loaPlot(..., panel = panel.kernelDensity)
#etc
panel.surfaceSmooth and other similar panel... functions
generate smoothed surfaces using supplied (x,y,z) data and pass
this to panel.loaLevelPlot to plot.
By default, panel.surfaceSmooth uses stats function
loess to generate a surface. Alternative smooths
can be set using the smooth.fun argument, and the surface
range can to controlled using the too.far argument.
panel.kernelDensity generates kernel density estimations
based on the supplied x and y data ranges. Because it is
density plot, it counts the number of z values. So, z values
are ignored. It is intended to be used in the form:
loaPlot(~x*y, ..., panel = panel.kernelDensity)
So, if any z information is supplied, users are warned that it has
been ignored, e.g:
loaPlot(z~x*y, ..., panel = panel.kernelDensity)
#warning generated
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. It is intended to be used in form:
loaPlot(z~x*y, ..., panel = panel.binPlot)
If no z values are supplied, as in:
loaPlot(~x*y, ..., panel = panel.binPlot)
... panel.binPlot resets statistic to length
(again with a warning) and gives a count of the number of elements in
each bin.
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.
(for panel.surfaceSmooth) mgcv package and too.far argument:
Wood, S.N. (2004) Stable and efficient multiple smoothing parameter
estimation for generalized additive models.Journal of the American
Statistical Association. 99:673-686.
Also http://www.maths.bath.ac.uk/~sw283/
In loa: panelPal
In lattice: xyplot,
levelplot,
panel.contourplot, lrect
## Example 1
## for data already set up for levelplot
loaPlot(volcano, panel=panel.loaLevelPlot)
## Example 2
## Surface smooth
loaPlot(copper~longitude*latitude, data= lat.lon.meuse,
panel=panel.surfaceSmooth, grid=TRUE,
too.far=0.1, col.regions=3:2)
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