`kppm(X, trend = ~1, clusters = "Thomas", covariates = NULL, ...)`

X

Point pattern (object of class

`"ppp"`

) to which the model
should be fitted.trend

An Rformula, with no left hand side,
specifying the form of the log intensity.

clusters

Character string determining the cluster model.
Partially matched.
Options are

`"Thomas"`

and `"MatClust"`

.covariates

The values of any spatial covariates (other than the Cartesian
coordinates) required by the model.
A named list of pixel images.

...

Arguments passed to

`thomas.estK`

or
`matclust.estK`

controlling the minimum contrast
fitting algorithm.- An object of class
`"kppm"`

representing the fitted model. There are methods for printing, plotting, predicting, simulating and updating objects of this class.

`X`

. The algorithm first estimates the intensity function
of the point process, by fitting a Poisson process with log intensity
of the form specified by the formula `trend`

.
Then the inhomogeneous $K$ function is estimated using the
fitted intensity. Finally the parameters of the cluster model
are estimated by the method of minimum contrast using the
inhomogeneous $K$ function.

Currently the only options for the cluster mechanism
are `clusters="Thomas"`

for the Thomas process
and `clusters="MatClust"`

for the Matern cluster process.

`plot.kppm`

,
`predict.kppm`

,
`simulate.kppm`

,
`update.kppm`

,
`thomas.estK`

,
`matclust.estK`

,
`mincontrast`

,
`Kinhom`

,
`ppm`

```
data(redwood)
kppm(redwood, ~1, "Thomas")
kppm(redwood, ~x, "MatClust")
```

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