Description Usage Arguments Details Value Note Author(s) References See Also Examples
Use the loess
function to interpolate the
twodimensional x
, y
, and 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.
1  interp.loess(x, y, z, gridlen = c(40,40), span = 0.1, ...)

x 
Vector of 
y 
Vector of 
z 
Vector of 
gridlen 
Size of the interpolated grid to be produced in x and y.
The default of 
span 
Kernel span argument to the 
... 
Further arguments to be passed to the

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 2d plotting functions
persp
, image
,
and contour
, etc.
The list contains...
x 
Vector of with 
y 
Vector of with 
z 

As mentioned above, the default span = 0.1
parameter is
significantly smaller that the default loess
setting.
This asserts a tacit assumption that
the input is densely packed and that the noise in z
's is 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 sparse, simply choose higher setting,
e.g., the default loess
setting of span =
0.75
, or a more intermediate setting of span = 0.5
as in the example below
Robert B. Gramacy, rbg@vt.edu, and Matt Taddy, mataddy@amazon.com
https://bobby.gramacy.com/r_packages/tgp/
interp
, loess
,
persp
, image
, contour
1 2 3 4 5 6 7 8 9  # random data
ed < exp2d.rand()
# higher span = 0.5 required because the data is sparse
# and was generated randomly
ed.g < interp.loess(ed$X[,1], ed$X[,2], ed$Z, span=0.5)
# perspective plot
persp(ed.g)

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