coef.mppm: Coefficients of Point Process Model Fitted to Multiple Point Patterns

Description

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

Usage

## S3 method for class 'mppm':
coef(object, \dots)

Arguments

object

The fitted point process model (an object of class "mppm")

...

Ignored.

Value

A vector containing the fitted coefficients.

Details

This function is a method for the generic function coef. The argument object must be a fitted point process model (object of class "mppm") produced by the fitting algorithm mppm). This represents a point process model that has been fitted to a list of several point pattern datasets. See mppm for information.

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.mppm to print a more useful description of the fitted model.

Examples

Run this code

data(waterstriders)
    H <- hyperframe(X=waterstriders)
    fit.Poisson <- mppm(X ~ 1, H)
    coef(fit.Poisson)

    # The single entry "(Intercept)" 
    # is the log of the fitted intensity of the Poisson process

    fit.Strauss <- mppm(X~1, H, Strauss(7))
    coef(fit.Strauss)

    # The two entries "(Intercept)" and "Interaction"
    # are respectively log(beta) and log(gamma)
    # in the usual notation for Strauss(beta, gamma, r)