coef.ppm

0th

Percentile

Coefficients of Fitted Point Process Model

Given a point process model fitted to a point pattern, extract the coefficients of the fitted model. A method for coef.

Keywords
spatial
Usage
coef.ppm(object, ...)
Arguments
object
The fitted point process model (an object of class "ppm")
...
Ignored.
Details

This function is a method for the generic function coef. The argument object must be a fitted point process model (object of class "ppm"). Such objects are produced by the maximum pseudolikelihood fitting algorithm mpl).

This function extracts the vector of coefficients of the fitted model. This is the estimate of the parameter vector $\theta$ such that the conditional intensity of the model is of the form $$\lambda(u,x) = \exp(\theta S(u,x))$$ where $S(u,x)$ is a (vector-valued) statistic.

For example, if the model object is the uniform Poisson process, then coef(object) will yield a single value (named "(Intercept)") which is the logarithm of the fitted intensity of the Poisson process.

Use print.ppm to print a more useful description of the fitted model.

Value

  • A vector containing the fitted coefficients.

See Also

print.ppm, ppm.object, mpl

Aliases
  • coef.ppm
Examples
require(spatstat)
    data(cells)

    poi <- mpl(cells, ~1, Poisson())
    coef(poi)
    # This is the log of the fitted intensity of the Poisson process

    str <- mpl(cells, ~1, Strauss(r=0.15), rbord=0.15)
    coef(str)
    # is a vector of two entries,
    # "(Intercept)" and "Interaction"
    # which are respectively log(beta) and log(gamma)
    # in the usual notation for Strauss(beta, gamma, r)
Documentation reproduced from package spatstat, version 1.3-4, License: GPL version 2 or newer

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