plot.tgp {tgp} | R Documentation |
A generic function for plotting of "tgp"
-class objects.
1-d posterior mean and error plots, 2-d posterior mean and
error image and perspective plots, and 3+-dimensional mean and error
image and perspective plots are supported via projection
and slicing.
## S3 method for class 'tgp': plot(x, pparts = TRUE, proj = NULL, slice = NULL, map = NULL, as = NULL, center = "mean", layout = "both", main = NULL, xlab = NULL, ylab = NULL, zlab = NULL, pc = "pc", method = "loess", gridlen = 40, span = 0.1, ...)
x |
"tgp" -class object that is the output of one of
the b* functions: blm , btlm
bgp , bgpllm , btgp , or
btgpllm |
pparts |
If TRUE , partition-regions are plotted (default),
otherwise they are not |
proj |
1-or-2-Vector describing the dimensions to be shown in a
projection. The argument is ignored for 1-d data, i.e., if x$d
== 1 . For 2-d data, no projection needs be specified— the
default argument (proj = NULL ) will result in a 2-d perspective
or image plot. 1-d projections of 2-d or higher data are are
supported, e.g., proj = c(2) would show the second variable
projection. For 3-d data or higher, proj=NULL defaults to
proj = c(1,2) which plots a 2-d projection for the first two
variables. Slices have priority over the projections—
see next argument (slice )— when non-null arguments are
provided for both. |
slice |
list object with x and z fields, which
are vectors of equal length describing the slice to be plotted, i.e.,
which z-values of the x$d - 2 inputs x$X and
x$XX should be fixed to in order to obtain a 2-d visualization.
For example, for 4-d data, slice = list(x=(2,4), z=c(0.2, 1.5) will
result in a 2-d plot of the first and third dimensions which have
the second and fourth slice fixed at 0.5 and 1.5. The default is
NULL , yielding to the proj argument. Argument is
ignored for 1-d data, i.e., if x$d == 1 |
map |
Optional 2-d map (longitude and latitude) from maps to be shown on top of image plots |
center |
Default center = "mean" causes the posterior
predictive mean to be plotted as the centering statistic.
Otherwise the median can be used with center = "med" , or the
kriging mean with center = "km" |
as |
Optional string indicator for plotting of adaptive sampling
statistics: specifying as = "alm" for ALM, as = "s2"
for predictive variance, as = "ks2" for expected kriging
variance, as = "alc" for ALC,
and as = "improv" for expected improvement (about the minimum).
The default as = NULL plots error-bars (1d-plots) or
error magnitudes (2d-plots), which is essentially the same as
as = "alm" |
layout |
Specify whether to plot the mean predictive surface
(layout = "surf" ), the error or adaptive sampling statistics
(layout = "as" ), or default (layout = "both" ) which shows both |
main |
Optional character string to add to the main title of the plot |
xlab |
Optional character string to add to the x label of the plots |
ylab |
Optional character string to add to the y label of the plots |
zlab |
Optional character string to add to the z label of the plots;
ignored unless pc = "p" |
pc |
Selects perspective-posterior mean and image-error plots
(pc = "pc" , the default) or a double–image plot (pc
= "c" ) |
method |
Method of interpolation onto a regular grid for
perspective and image plotting of 2-d slices and projections.
Ignored for 1-d plots and projections.
Default is method = "loess" which results in the
use of the built-in loess function via the provided
interp.loess interface. The lowess kernel span can be specified
with the span parameter to this function. Another
option, method = "akima" uses the akima function
interp.old. In general, akima provides a
better (linear) interpolation, but the current version(s) has been
buggy (see notes below), and so is not the default |
gridlen |
Number of regular grid points for 2-d slices and
projections. Default is gridlen = 40 . Ignored for 1-d plots
and projections |
span |
Span for loess kernel. This argument is ignored for
1-d plots and projections, and 2-d plots with
method = "akima" . The tgp package default (span =
0.1 ) is set lower than the loess default.
Smaller spans can lead to warnings from loess
when the data or predictive locations are sparse and ugly plots may
result. In this case, try increasing the span, or use the
method = "akima" option |
... |
Extra arguments to 1-d (plot ) and 2-d plotting functions
persp and image |
The only output of this function is beautiful plots.
This plotting function is provided with the intention that it
will be used as an aid in the visualization of "tgp"
-class
objects. Users are encouraged to use the source code for
this function in order to develop custom plotting functions.
There seems to be two bugs in the interp
function of
the akima package. The first, benign, bug causes NA
s
to appear when plotting 2-d surfaces. The result is white
rectangles (or “holes”) in the surfaces. This affect is
most severe when x$X
and x$XX
together form a
regular grid, or close to a regular grid. The second, malign,
bug can cause segmentation faults when plotting projections,
causing the entire R session to crash.
Please note this is not a bug in the tgp package,
rather it is a bug in the akima package.
Instead, interpolation by the loess
function is provided
as a workaround, and is the default (method = "loess"
).
1-d projections for 3-d or higher data are also available
by specifying a 1-d projection argument (e.g. proj=c(1)
for projecting onto the first input variable).
For examples, see vignette("tgp")
and the
help files of those functions in "See Also", below
Robert B. Gramacy rbgramacy@ams.ucsc.edu
http://www.ams.ucsc.edu/~rbgramacy/tgp.html
plot
, bgpllm
, btlm
,
blm
, bgp
, btgpllm
, predict.tgp
,
tgp.trees
, mapT
, interp
,
loess