bw.locppm {spatstat.local} | R Documentation |
Cross Validated Bandwidth Selection for Locally Fitted Point Process Model
Description
Uses cross-validation to select a smoothing bandwidth for locally fitting a Poisson or Gibbs point process model.
Usage
bw.locppm(...,
method = c("fft", "exact", "taylor"),
srange = NULL, ns = 9, sigma = NULL,
additive = TRUE,
verbose = TRUE)
Arguments
... |
Arguments passed to |
method |
Method of calculation. The default |
srange |
Range of values of the smoothing parameter |
ns |
Number of values of the smoothing parameter |
sigma |
Vector of values of the smoothing parameter to be searched.
Overrides the values of |
additive |
Logical value indicating whether to calculate the leverage
approximation on the scale of the intensity ( |
verbose |
Logical value indicating whether to display progress reports. |
Details
This function determines the optimal value of the smoothing
parameter sigma
to be used in a call to locppm
.
The function locppm
fits
a Poisson or Gibbs point process model
to point pattern data by local composite likelihood.
The degree of local smoothing is controlled by a smoothing parameter
sigma
which is an argument to locppm
.
This function bw.locppm
determines the optimal value of
sigma
by cross-validation.
For each value of sigma
in a search interval,
the function bw.locppm
fits the model locally
with smoothing bandwidth sigma
,
and evaluates the composite likelihood cross-validation criterion
LCV(sigma)
defined in Baddeley (2016), section 3.2.
The value of sigma
which maximises LCV(sigma)
is returned.
Value
A numerical value giving the selected bandwidth.
The result also belongs to the class "bw.optim"
which can be plotted.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au.
References
Baddeley, A. (2017) Local composite likelihood for spatial point patterns. Spatial Statistics 22, 261–295. DOI: 10.1016/j.spasta.2017.03.001
Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.
See Also
Examples
Ns <- if(interactive()) 16 else 2
b <- bw.locppm(swedishpines, ~1, srange=c(2.5,4.5), ns=Ns)
b
plot(b)