vcov.kppm
From spatstat v1.34-1
by Adrian Baddeley
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.
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.
See Also
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)))
Community examples
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