# vcov.kppm

From spatstat v1.28-1
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))`

.

##### 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)
vc <- vcov(fit)
sd <- sqrt(diag(vc))
t(coef(fit) + 1.96 * outer(sd, c(lower=-1, upper=1)))
vcov(fit, what="corr")
```

*Documentation reproduced from package spatstat, version 1.28-1, License: GPL (>= 2)*

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