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.
## S3 method for class 'kppm'
vcov(object, ...,
what=c("vcov", "corr", "fisher", "internals"),
fast = NULL, rmax = NULL, eps.rmax = 0.01,
verbose = TRUE)object |
A fitted cluster point process model (an object of class
|
... |
Ignored. |
what |
Character string (partially-matched)
that specifies what matrix is returned.
Options are |
fast |
Logical specifying whether tapering (using sparse matrices from Matrix) should be used to speed up calculations. Warning: This is expected to underestimate the true asymptotic variances/covariances. |
rmax |
Optional. The dependence range. Not usually specified by the
user. Only used when |
eps.rmax |
Numeric. A small positive number which is used to determine |
verbose |
Logical value indicating whether to print progress reports during very long calculations. |
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.
A square matrix.
Abdollah Jalilian and Rasmus Waagepetersen. Ported to spatstat by Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Ege Rubak rubak@math.aau.dk.
Waagepetersen, R. (2007) Estimating functions for inhomogeneous spatial point processes with incomplete covariate data. Biometrika 95, 351–363.
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)))
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