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

```
# S3 method for 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
`"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 information matrix.

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 `fast=TRUE`

.

eps.rmax

Numeric. A small positive number which is used to determine `rmax`

from the tail behaviour of the pair correlation function when fast
option (`fast=TRUE`

) is used. Namely
`rmax`

is the smallest value of \(r\)
at which \((g(r)-1)/(g(0)-1)\)
falls below `eps.rmax`

.
Only used when `fast=TRUE`

.
Ignored if `rmax`

is provided.

verbose

Logical value indicating whether to print progress reports during very long calculations.

A square matrix.

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.

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

# NOT RUN { 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))) # }