interp.loess {tgp} | R Documentation |
Use the loess
function to interpolate the
two-dimensional x, y, z data onto a uniform grid. The output
produced is an object directly usable by the plotting functions
persp
, image
,
and contour
, etc.
This function is designed as an alternative to the
interp
functions from the akima
library.
interp.loess(x, y, z, gridlen = 40, span = 0.1, ...)
x |
Vector of X spatial input locations |
y |
Vector of Y spatial input locations |
z |
Vector of Z responses interpreted as
Z = f(X,Y) |
gridlen |
Size of the interpolated grid to be produced.
The default of gridlen = 40 causes a 40 * 40
grid of X , Y , and Z values to be computed. |
span |
Kernel span argument to the loess
function with default setting span = 0.1 set significantly lower than the
the loess default – see note below. |
... |
Further arguments to be passed to the
loess function |
Uses expand.grid
function to produce a uniform
grid of size gridlen
with domain equal to the rectangle implied
by X
and Y
. Then, a loess
a smoother
is fit to the data Z = f(X,Y)
. Finally,
predict.loess
is used to predict onto the grid.
The output is a list compatible with the 2-d plotting functions
persp
, image
,
and contour
, etc.
The list contains...
x |
Vector of with length(x) == gridlen of increasing
X grid locations |
y |
Vector of with length(y) == gridlen of increasing
Y grid locations |
z |
matrix of interpolated responses Z = f(X,Y)
where z[i,j] contains an estimate of f(x[i],y[j]) |
As mentioned above, the default span = 0.1
parameter is
signifigantly smaller that the default loess
setting.
This asserts a tacit assumption that
the input is densly packed and that the variance in the data is be small.
Such should be the case when the data are output from a tgp regression –
this function was designed specifically for this situation.
For data that is random or sparce, simply choose higher setting,
e.g., the default interp
setting of span =
0.75
, or a more intermediate setting of span = 0.5
as in the example below
Robert B. Gramacy rbgramacy@ams.ucsc.edu
http://www.ams.ucsc.edu/~rbgramacy/tgp.php
interp
, loess
,
persp
, image
, contour
# random data ed <- exp2d.rand() # higher span = 0.5 required becuase the data is sparce # and was generated randomly ed.g <- interp.loess(ed$X[,1], ed$X[,2], ed$Z, span=0.5) # perspective plot persp(ed.g)