spatstat (version 1.34-1)

vcov.kppm: Variance-Covariance Matrix for a Fitted Cluster Point Process Model

Description

Returns the variance-covariance matrix of the estimates of the parameters of a fitted cluster point process model.

Usage

## S3 method for class 'kppm':
vcov(object, ...,
          what=c("vcov", "corr", "fisher", "internals"))

Arguments

object

A fitted cluster point process model (an object of class "kppm".)

...

Ignored.

what

Character string (partially-matched) that specifies what matrix is returned. Options are "vcov" for the variance-covariance matrix, "corr" for the correlation matrix, and "fisher" for the Fisher infor

Value

• A square matrix.

Details

This function computes the asymptotic variance-covariance matrix of the estimates of the canonical (regression) parameters in the cluster point process model object. It is a method for the generic function vcov. The result is an n * n matrix where n = length(coef(model)).

To calculate a confidence interval for a regression parameter, use confint as shown in the examples.

References

Waagepetersen, R. (2007) Estimating functions for inhomogeneous spatial point processes with incomplete covariate data. Biometrika 95, 351--363.

See Also

kppm, vcov, vcov.ppm

Examples

data(redwood)
   fit <- kppm(redwood, ~ x + y)
   vcov(fit)
   vcov(fit, what="corr")

   # confidence interval
   confint(fit)
   # cross-check the confidence interval by hand:
   sd <- sqrt(diag(vcov(fit)))
   t(coef(fit) + 1.96 * outer(sd, c(lower=-1, upper=1)))