# vargamma.estK

##### Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel

Fits the Neyman-Scott cluster point process, with Variance Gamma kernel, to a point pattern dataset by the Method of Minimum Contrast.

##### Usage

```
vargamma.estK(X, startpar=c(kappa=1,eta=1), nu.ker = -1/4, lambda=NULL,
q = 1/4, p = 2, rmin = NULL, rmax = NULL, ...)
```

##### Arguments

- X
- Data to which the model will be fitted. Either a point pattern or a summary statistic. See Details.
- startpar
- Vector of starting values for the parameters of the model.
- nu.ker
- Numerical value controlling the shape of the tail of the clusters.
A number greater than
`-1/2`

. - lambda
- Optional. An estimate of the intensity of the point process.
- q,p
- Optional. Exponents for the contrast criterion.
- rmin, rmax
- Optional. The interval of $r$ values for the contrast criterion.
- ...
- Optional arguments passed to
`optim`

to control the optimisation algorithm. See Details.

##### Details

This algorithm fits the Neyman-Scott Cluster point process model with Variance Gamma kernel (Jalilian et al, 2011) to a point pattern dataset by the Method of Minimum Contrast, using the $K$ function.

The argument `X`

can be either
[object Object],[object Object]

The algorithm fits the Neyman-Scott Cluster point process
with Variance Gamma kernel to `X`

,
by finding the parameters of the model
which give the closest match between the
theoretical $K$ function of the model
and the observed $K$ function.
For a more detailed explanation of the Method of Minimum Contrast,
see `mincontrast`

.
The Neyman-Scott cluster point process with Variance Gamma
kernel is described in Jalilian et al (2011).
It is a cluster process formed by taking a
pattern of parent points, generated according to a Poisson process
with intensity $\kappa$, and around each parent point,
generating a random number of offspring points, such that the
number of offspring of each parent is a Poisson random variable with mean
$\mu$, and the locations of the offspring points of one parent
have a common distribution described in Jalilian et al (2011).

If the argument `lambda`

is provided, then this is used
as the value of the point process intensity $\lambda$.
Otherwise, if `X`

is a
point pattern, then $\lambda$
will be estimated from `X`

.
If `X`

is a summary statistic and `lambda`

is missing,
then the intensity $\lambda$ cannot be estimated, and
the parameter $\mu$ will be returned as `NA`

.

The remaining arguments `rmin,rmax,q,p`

control the
method of minimum contrast; see `mincontrast`

.

The corresponding model can be simulated using `rVarGamma`

.

The parameter `eta`

appearing in `startpar`

is equivalent to the
scale parameter `omega`

used in `rVarGamma`

.
Homogeneous or inhomogeneous Neyman-Scott/VarGamma models can also be
fitted using the function `kppm`

and the fitted models
can be simulated using `simulate.kppm`

.

The optimisation algorithm can be controlled through the
additional arguments `"..."`

which are passed to the
optimisation function `optim`

. For example,
to constrain the parameter values to a certain range,
use the argument `method="L-BFGS-B"`

to select an optimisation
algorithm that respects box constraints, and use the arguments
`lower`

and `upper`

to specify (vectors of) minimum and
maximum values for each parameter.

##### Value

- An object of class
`"minconfit"`

. There are methods for printing and plotting this object. It contains the following main components: par Vector of fitted parameter values. fit Function value table (object of class `"fv"`

) containing the observed values of the summary statistic (`observed`

) and the theoretical values of the summary statistic computed from the fitted model parameters.

##### References

Jalilian, A., Guan, Y. and Waagepetersen, R. (2011) Decomposition of variance for spatial Cox processes. Manuscript submitted for publication.

Waagepetersen, R. (2007)
An estimating function approach to inference for
inhomogeneous Neyman-Scott processes.
*Biometrics* **63**, 252--258.

##### See Also

`kppm`

,
`vargamma.estpcf`

,
`lgcp.estK`

,
`thomas.estK`

,
`cauchy.estK`

,
`mincontrast`

,
`Kest`

,
`Kmodel`

.

`rVarGamma`

to simulate the model.

##### Examples

```
<testonly>u <- vargamma.estK(redwood, startpar=c(kappa=15, eta=0.075))</testonly>
if(interactive()) {
u <- vargamma.estK(redwood)
u
plot(u)
}
```

*Documentation reproduced from package spatstat, version 1.29-0, License: GPL (>= 2)*