Fits a Neyman-Scott cluster process or Cox point process model using local minimum contrast.
locmincon(..., sigma = NULL, f = 1/4, verbose = TRUE,
localstatargs = list(), LocalStats = NULL,
tau = NULL)Object of class "locmincon".
Arguments passed to kppm
to determine the template homogeneous model.
Standard deviation of Gaussian kernel for local likelihood.
Argument passed to bw.frac to
compute a value for sigma if it is missing or NULL.
Logical. If TRUE, print progress reports.
Optional. List of arguments to be passed to the local statistic
localK,
localKinhom,
localpcf or
localpcfinhom.
Optional. Values of the local statistics, if they have already been computed.
Optional. Bandwidth for smoothing the fitted cluster parameters.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au.
The template or homogeneous model is first fitted by
kppm.
The statistic used to fit the template model is determined
(as explained in the help for kppm)
by the arguments statistic and trend.
The local version of this statistic is then computed.
If statistic="K" and trend=~1
for example, the template model is fitted
using the \(K\) function Kest,
and the local version is the local \(K\) function
localK. The possibilities are:
statistic | stationary? | template | local |
"K" | yes | Kest | localK |
"K" | no | Kinhom | localKinhom |
"pcf" | yes | pcf | localpcf |
"pcf" | no | pcfinhom | localpcfinhom |
These local functions, one for each data point, are then spatially
averaged, using a Gaussian kernel with standard deviation sigma.
Finally the model is fitted to each of the averaged local functions
to obtain a local fit at each data point.
Baddeley, A. (2017) Local composite likelihood
Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.
loccit
X <- redwood[owin(c(0,1), c(-1,-1/2))]
fit <- locmincon(X, ~1, "Thomas", sigma=0.07)
fit
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