methods.zclustermodel: Methods for Cluster Models

In spatstat.core: Core Functionality of the 'spatstat' Family

Methods for Cluster Models

Description

Methods for the experimental class of cluster models.

Usage

 ## S3 method for class 'zclustermodel'
pcfmodel(model, ...)

 ## S3 method for class 'zclustermodel'
Kmodel(model, ...)

 ## S3 method for class 'zclustermodel'
intensity(X, ...)

 ## S3 method for class 'zclustermodel'
predict(object, ...,
                  locations, type = "intensity", ngrid = NULL)

 ## S3 method for class 'zclustermodel'
print(x, ...)

 ## S3 method for class 'zclustermodel'
clusterradius(model,...,thresh=NULL, precision=FALSE)

 ## S3 method for class 'zclustermodel'
reach(x, ..., epsilon)

Arguments

model,object,x,X	Object of class "zclustermodel".
...	Arguments passed to other methods.
locations	Locations where prediction should be performed. A window or a point pattern.
type	Currently must equal "intensity".
ngrid	Pixel grid dimensions for prediction, if locations is a rectangle or polygon.
thresh,epsilon	Tolerance thresholds
precision	Logical value stipulating whether the precision should also be returned.

Details

Experimental.

Value

Same as for other methods.

Author(s)

\adrian

See Also

zclustermodel

Examples

  m <- zclustermodel("Thomas", kappa=10, mu=5, scale=0.1)
  m2 <- zclustermodel("VarGamma", kappa=10, mu=10, scale=0.1, nu=0.7)
  m
  m2
  g <- pcfmodel(m)
  g(0.2)
  g2 <- pcfmodel(m2)
  g2(1)
  Z <- predict(m, locations=square(2))
  Z2 <- predict(m2, locations=square(1))
  varcount(m, square(1))
  varcount(m2, square(1))

spatstat.core documentation built on May 18, 2022, 9:05 a.m.