# dppm

##### Fit Determinantal Point Process Model

Fit a determinantal point process model to a point pattern.

##### Usage

```
dppm(formula, family, data=NULL,
...,
startpar = NULL,
method = c("mincon", "clik2", "palm"),
weightfun=NULL,
control=list(),
algorithm="Nelder-Mead",
statistic="K",
statargs=list(),
rmax = NULL,
covfunargs=NULL,
use.gam=FALSE,
nd=NULL, eps=NULL)
```

##### Arguments

- formula
A

`formula`

in the R language specifying the data (on the left side) and the form of the model to be fitted (on the right side). For a stationary model it suffices to provide a point pattern without a formula. See Details.- family
Information specifying the family of point processes to be used in the model. Typically one of the family functions

`dppGauss`

,`dppMatern`

,`dppCauchy`

,`dppBessel`

or`dppPowerExp`

. Alternatively a character string giving the name of a family function, or the result of calling one of the family functions. See Details.- data
The values of spatial covariates (other than the Cartesian coordinates) required by the model. A named list of pixel images, functions, windows, tessellations or numeric constants.

- …
Additional arguments. See Details.

- startpar
Named vector of starting parameter values for the optimization.

- method
The fitting method. Either

`"mincon"`

for minimum contrast,`"clik2"`

for second order composite likelihood, or`"palm"`

for Palm likelihood. Partially matched.- weightfun
Optional weighting function \(w\) in the composite likelihood or Palm likelihood. A

`function`

in the R language. See Details.- control
List of control parameters passed to the optimization function

`optim`

.- algorithm
Character string determining the mathematical optimisation algorithm to be used by

`optim`

. See the argument`method`

of`optim`

.- statistic
Name of the summary statistic to be used for minimum contrast estimation: either

`"K"`

or`"pcf"`

.- statargs
Optional list of arguments to be used when calculating the

`statistic`

. See Details.- rmax
Maximum value of interpoint distance to use in the composite likelihood.

- covfunargs,use.gam,nd,eps
Arguments passed to

`ppm`

when fitting the intensity.

##### Details

This function fits a determinantal point process model to a point pattern dataset as described in Lavancier et al. (2015).

The model to be fitted is specified by the arguments
`formula`

and `family`

.

The argument `formula`

should normally be a `formula`

in the
R language. The left hand side of the formula
specifies the point pattern dataset to which the model should be fitted.
This should be a single argument which may be a point pattern
(object of class `"ppp"`

) or a quadrature scheme
(object of class `"quad"`

). The right hand side of the formula is called
the `trend`

and specifies the form of the
*logarithm of the intensity* of the process.
Alternatively the argument `formula`

may be a point pattern or quadrature
scheme, and the trend formula is taken to be `~1`

.

The argument `family`

specifies the family of point processes
to be used in the model.
It is typically one of the family functions
`dppGauss`

, `dppMatern`

,
`dppCauchy`

, `dppBessel`

or `dppPowerExp`

.
Alternatively it may be a character string giving the name
of a family function, or the result of calling one of the
family functions. A family function belongs to class
`"detpointprocfamilyfun"`

. The result of calling a family
function is a point process family, which belongs to class
`"detpointprocfamily"`

.

The algorithm first estimates the intensity function
of the point process using `ppm`

.
If the trend formula is `~1`

(the default if a point pattern or quadrature
scheme is given rather than a `"formula"`

)
then the model is *homogeneous*. The algorithm begins by
estimating the intensity as the number of points divided by
the area of the window.
Otherwise, the model is *inhomogeneous*.
The algorithm begins by fitting a Poisson process with log intensity
of the form specified by the formula `trend`

.
(See `ppm`

for further explanation).

The interaction parameters of the model are then fitted either by minimum contrast estimation, or by maximum composite likelihood.

- Minimum contrast:
If

`method = "mincon"`

(the default) interaction parameters of the model will be fitted by minimum contrast estimation, that is, by matching the theoretical \(K\)-function of the model to the empirical \(K\)-function of the data, as explained in`mincontrast`

.For a homogeneous model (

`trend = ~1`

) the empirical \(K\)-function of the data is computed using`Kest`

, and the interaction parameters of the model are estimated by the method of minimum contrast.For an inhomogeneous model, the inhomogeneous \(K\) function is estimated by

`Kinhom`

using the fitted intensity. Then the interaction parameters of the model are estimated by the method of minimum contrast using the inhomogeneous \(K\) function. This two-step estimation procedure is heavily inspired by Waagepetersen (2007).If

`statistic="pcf"`

then instead of using the \(K\)-function, the algorithm will use the pair correlation function`pcf`

for homogeneous models and the inhomogeneous pair correlation function`pcfinhom`

for inhomogeneous models. In this case, the smoothing parameters of the pair correlation can be controlled using the argument`statargs`

, as shown in the Examples.Additional arguments

`…`

will be passed to`clusterfit`

to control the minimum contrast fitting algorithm.- Composite likelihood:
If

`method = "clik2"`

the interaction parameters of the model will be fitted by maximising the second-order composite likelihood (Guan, 2006). The log composite likelihood is $$ \sum_{i,j} w(d_{ij}) \log\rho(d_{ij}; \theta) - \left( \sum_{i,j} w(d_{ij}) \right) \log \int_D \int_D w(\|u-v\|) \rho(\|u-v\|; \theta)\, du\, dv $$ where the sums are taken over all pairs of data points \(x_i, x_j\) separated by a distance \(d_{ij} = \| x_i - x_j\|\) less than`rmax`

, and the double integral is taken over all pairs of locations \(u,v\) in the spatial window of the data. Here \(\rho(d;\theta)\) is the pair correlation function of the model with cluster parameters \(\theta\).The function \(w\) in the composite likelihood is a weighting function and may be chosen arbitrarily. It is specified by the argument

`weightfun`

. If this is missing or`NULL`

then the default is a threshold weight function, \(w(d) = 1(d \le R)\), where \(R\) is`rmax/2`

.- Palm likelihood:
If

`method = "palm"`

the interaction parameters of the model will be fitted by maximising the Palm loglikelihood (Tanaka et al, 2008) $$ \sum_{i,j} w(x_i, x_j) \log \lambda_P(x_j \mid x_i; \theta) - \int_D w(x_i, u) \lambda_P(u \mid x_i; \theta) {\rm d} u $$ with the same notation as above. Here \(\lambda_P(u|v;\theta\) is the Palm intensity of the model at location \(u\) given there is a point at \(v\).

In all three methods, the optimisation is performed by the generic
optimisation algorithm `optim`

.
The behaviour of this algorithm can be modified using the
argument `control`

.
Useful control arguments include
`trace`

, `maxit`

and `abstol`

(documented in the help for `optim`

).

Finally, it is also possible to fix any parameters desired before the
optimisation by specifying them as `name=value`

in the call to the family function. See Examples.

##### Value

An object of class `"dppm"`

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

##### References

Lavancier, F. Moller, J. and Rubak, E. (2015)
Determinantal point process models and statistical inference
*Journal of the Royal Statistical Society, Series B*
**77**, 853--977.

Guan, Y. (2006)
A composite likelihood approach in fitting spatial point process
models.
*Journal of the American Statistical Association*
**101**, 1502--1512.

Tanaka, U. and Ogata, Y. and Stoyan, D. (2008)
Parameter estimation and model selection for
Neyman-Scott point processes.
*Biometrical Journal* **50**, 43--57.

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

##### See Also

methods for `dppm`

objects:
`plot.dppm`

,
`fitted.dppm`

,
`predict.dppm`

,
`simulate.dppm`

,
`methods.dppm`

,
`as.ppm.dppm`

,
`Kmodel.dppm`

,
`pcfmodel.dppm`

.

Minimum contrast fitting algorithm:
higher level interface `clusterfit`

;
low-level algorithm `mincontrast`

.

Deterimantal point process models:
`dppGauss`

,
`dppMatern`

,
`dppCauchy`

,
`dppBessel`

,
`dppPowerExp`

,

Summary statistics:
`Kest`

,
`Kinhom`

,
`pcf`

,
`pcfinhom`

.

See also `ppm`

##### Examples

```
# NOT RUN {
jpines <- residualspaper$Fig1
# }
# NOT RUN {
dppm(jpines ~ 1, dppGauss)
dppm(jpines ~ 1, dppGauss, method="c")
dppm(jpines ~ 1, dppGauss, method="p")
# Fixing the intensity to lambda=2 rather than the Poisson MLE 2.04:
dppm(jpines ~ 1, dppGauss(lambda=2))
if(interactive()) {
# The following is quite slow (using K-function)
dppm(jpines ~ x, dppMatern)
}
# much faster using pair correlation function
dppm(jpines ~ x, dppMatern, statistic="pcf", statargs=list(stoyan=0.2))
# Fixing the Matern shape parameter to nu=2 rather than estimating it:
dppm(jpines ~ x, dppMatern(nu=2))
# }
```

*Documentation reproduced from package spatstat, version 1.63-0, License: GPL (>= 2)*