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, ...)

Arguments

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.

Value

A vector containing the fitted coefficients.

Author(s)

Adrian Baddeley adrian.baddeley@uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner rolf@math.unb.ca http://www.math.unb.ca/~rolf

See Also

print.mppm, mppm

Examples

    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)