# ppm.object

##### Class of Fitted Point Process Models

A class `ppm`

to represent a fitted stochastic model
for a point process. The output of `mpl`

.

- Keywords
- spatial

##### Details

An object of class `ppm`

represents a stochastic point process
model that has been fitted to a point pattern dataset.
Typically it is the output of the maximum pseudolikelihood model fitter,
`mpl`

.

There are methods `print.ppm`

,
`plot.ppm`

, `predict.ppm`

, `fitted.ppm`

and `coef.ppm`

for the generic functions
`print`

, `plot`

, `predict`

,
`fitted`

and `coef`

respectively.

See also (for example) `Strauss`

to understand how to specify
a point process model with unknown parameters.

Information about the data (to which the model was fitted)
can be extracted using `data.ppm`

, `dummy.ppm`

and `quad.ppm`

.

If you really need to get at the internals,
a `ppm`

object contains at least the following entries:
`coef`

the fitted regular parameters (as returned by
`glm`

)
`trend`

the trend formula or `NULL`

`interaction`

the point process interaction family
(an object of class `"interact"`

)
or `NULL`

`Q`

the quadrature scheme used
`maxlogpl`

the maximised value of log pseudolikelihood
`correction`

name of edge correction method used
}
See `mpl`

for explanation of these concepts.
The irregular parameters (e.g. the interaction radius of the
Strauss process) are encoded in the `interaction`

entry.
However see the Warnings.

##### Warnings

The internal representation may change in the next few releases
of the `spatstat`

package.

##### See Also

`mpl`

,
`coef.ppm`

,
`fitted.ppm`

,
`print.ppm`

,
`predict.ppm`

,
`plot.ppm`

.

##### Examples

```
library(spatstat)
data(cells)
fit <- mpl(cells, ~ x, Strauss(0.1), correction="periodic")
fit
coef(fit)
pred <- predict(fit)
pred <- predict(fit, nx=50, ny=50, type="trend")
plot(fit)
```

*Documentation reproduced from package spatstat, version 1.3-2, License: GPL version 2 or newer*