smooth.ppp

0th

Percentile

Spatial smoothing of observations at irregular points

Performs spatial smoothing of numeric values observed at a set of irregular locations.

Keywords
methods, smooth, spatial
Usage
smooth.ppp(X, ..., weights = rep(1, X$n))
Arguments
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.
Details

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 $v_1,\ldots,v_n$ at locations $x_1,\ldots,x_n$ respectively, then the smoothed value at a location $u$ is (ignoring edge corrections) $$g(u) = \frac{\sum_i k(u-x_i) v_i}{\sum_i k(u-x_i)}$$ 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).

Value

  • A pixel image (object of class "im"). Pixel values are values of the interpolated function.

See Also

density.ppp, ppp.object, im.object. To perform interpolation, see the akima package.

Aliases
  • smooth.ppp
Examples
# 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))
Documentation reproduced from package spatstat, version 1.12-5, License: GPL (>= 2)

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