bw.smoothppp | R Documentation |
Uses least-squares cross-validation to select a smoothing bandwidth for spatial smoothing of marks.
bw.smoothppp(X, nh = spatstat.options("n.bandwidth"), hmin=NULL, hmax=NULL, warn=TRUE, kernel="gaussian")
X |
A marked point pattern with numeric marks. |
nh |
Number of trial values of smoothing bandwith |
hmin, hmax |
Optional. Numeric values.
Range of trial values of smoothing bandwith |
warn |
Logical. If |
kernel |
The smoothing kernel.
A character string specifying the smoothing kernel
(current options are |
This function selects an appropriate bandwidth for the nonparametric
smoothing of mark values using Smooth.ppp
.
The argument X
must be a marked point pattern
with a vector or data frame of marks. All mark values must be numeric.
The bandwidth is selected by least-squares cross-validation. Let y[i] be the mark value at the ith data point. For a particular choice of smoothing bandwidth, let y*[i] be the smoothed value at the ith data point. Then the bandwidth is chosen to minimise the squared error of the smoothed values sum (y[i] - y*[i])^2.
The result of bw.smoothppp
is a numerical value giving the selected bandwidth sigma
.
The result also belongs to the class "bw.optim"
allowing it to be printed and plotted. The plot shows the cross-validation
criterion as a function of bandwidth.
The range of values for the smoothing bandwidth sigma
is set by the arguments hmin, hmax
. There is a sensible default,
based on the nearest neighbour distances.
If the optimal bandwidth is achieved at an endpoint of the
interval [hmin, hmax]
, the algorithm will issue a warning
(unless warn=FALSE
). If this occurs, then it is probably advisable
to expand the interval by changing the arguments hmin, hmax
.
Computation time depends on the number nh
of trial values
considered, and also on the range [hmin, hmax]
of values
considered, because larger values of sigma
require
calculations involving more pairs of data points.
A numerical value giving the selected bandwidth.
The result also belongs to the class "bw.optim"
which can be plotted.
and \rolf
Smooth.ppp
data(longleaf) b <- bw.smoothppp(longleaf) b plot(b)
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