| smooth.ppp {spatstat} | R Documentation |
Performs spatial smoothing of numeric values observed at a set of irregular locations.
smooth.ppp(X, ..., weights = rep(1, X$n))
X |
A marked point pattern (object of class "ppp"). |
... |
Arguments passed to density.ppp
to control the kernel smoothing. |
weights |
Optional weights attached to the observations. |
This function performs spatial smoothing of numeric values observed at a set of irregular locations.
Smoothing is performed by Gaussian kernel weighting. If the observed values are v[1],...,v[n] at locations x[1],...,x[n] respectively, then the smoothed value at a location u is (ignoring edge corrections)
g(u) = (sum of k(u-x[i]) v[i])/(sum of k(u-x[i]))
where k is a Gaussian kernel.
The argument X must be a marked point pattern (object
of class "ppp", see ppp.object)
in which the points are the observation locations,
and the marks are the numeric values observed at each point.
The numerator and denominator are computed by density.ppp.
The arguments ... control the smoothing kernel parameters
and determine whether edge correction is applied.
See density.ppp.
The optional argument weights allows numerical weights to
be applied to the data (the weights appear in both the sums
in the equation above).
A pixel image (object of class "im").
Pixel values are values of the interpolated function.
Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner r.turner@auckland.ac.nz
density.ppp,
ppp.object,
im.object.
To perform interpolation, see the akima package.
# Longleaf data - tree locations, marked by tree diameter data(longleaf) # Local smoothing of tree diameter Z <- smooth.ppp(longleaf) # Kernel bandwidth sigma=5 plot(smooth.ppp(longleaf, 5))