bw.smoothppp(X, nh = spatstat.options("n.bandwidth"), hmin=NULL, hmax=NULL, warn=TRUE)sigma
to consider. The default is 32.
sigma
to consider. There is a sensible default.
TRUE, issue a warning if the minimum of
the cross-validation criterion occurs at one of the ends of the
search interval.
"bw.optim"
which can be plotted.
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 $i$th data point.
For a particular choice of smoothing bandwidth,
let $y*[i]$ be the smoothed value at the $i$th 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.
Smooth.ppp
data(longleaf)
b <- bw.smoothppp(longleaf)
b
plot(b)
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