R: Methods for Cluster Models

methods.zclustermodel {spatstat.core} | R Documentation

## 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 Baddeley Adrian.Baddeley@curtin.edu.au

### 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))

[Package spatstat.core version 2.4-4 Index]