methods.locppm {spatstat.local}

Methods for Local Gibbs Models

Description

Methods for various generic functions, for the class "locppm" of locally fitted Gibbs point process models.

Usage

  ## S3 method for class 'locppm'
as.interact(object)

  ## S3 method for class 'locppm'
as.ppm(object)

  ## S3 method for class 'locppm'
coef(object, ..., which = c("local", "homogeneous"))

  ## S3 method for class 'locppm'
confint(object, parm, level = 0.95, ..., which = c("local", "homogeneous"))

  ## S3 method for class 'locppm'
is.poisson(x)

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

Arguments

object, x

A locally-fitted Gibbs point process model (object of class "locppm").

...

Additional arguments passed to the default method (for confint.locppm) or ignored (by coef.locppm).

which

Character string determining whether to perform calculations for the local Gibbs model (which="local", the default) or the corresponding homogeneous Gibbs model (which="homogeneous").

parm

The parameter or parameters for which a confidence interval is desired. A character string or character vector matching the names of coef(object), or an index or index vector that can be applied to coef(object).

level

Confidence level: a number between 0 and 1.

Details

Objects of class "locppm" represent locally fitted Gibbs point process models.

The functions documented here provided methods for this class, for the generic functions as.interact, as.ppm, coef, confint, is.poisson and print.

For the coef and confint methods, the calculations can be performed either on the locally fitted model or on its homogeneous equivalent, by changing the argument which.

Value

as.interact returns an interaction structure (object of class "interact").

as.ppm returns a fitted Gibbs model (object of class "ppm").

coef and confint return a numeric vector if which="homogeneous" and an object of class "ssf" if which="local".

is.poisson returns a logical value.

print returns NULL.

Author(s)

Adrian Baddeley

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

locppm

Examples

   fit <- locppm(swedishpines, ~1, sigma=9, nd=20,
                 vcalc="full", locations="coarse")
   fit
   is.poisson(fit)
   coef(fit)
   coef(fit, which="homogeneous")
   confint(fit)
   confint(fit, which="homogeneous")
   as.ppm(fit)
   as.interact(fit)