vcov.kppm

0th

Percentile

Variance-Covariance Matrix for a Fitted Cluster Point Process Model

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

Keywords
models, methods, spatial
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
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.

Value

• A square matrix.

References

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

kppm, vcov, vcov.ppm

• vcov.kppm
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)))
Documentation reproduced from package spatstat, version 1.36-0, License: GPL (>= 2)

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