# model.frame.ppm

##### Extract the Variables in a Point Process Model

Given a fitted point process model, this function returns a data frame containing all the variables needed to fit the model using the Berman-Turner device.

##### Usage

```
# S3 method for ppm
model.frame(formula, ...)
``` # S3 method for kppm
model.frame(formula, ...)

# S3 method for dppm
model.frame(formula, ...)

# S3 method for lppm
model.frame(formula, ...)

##### Arguments

- formula
A fitted point process model. An object of class

`"ppm"`

or`"kppm"`

or`"dppm"`

or`"lppm"`

.- …
Additional arguments passed to

`model.frame.glm`

.

##### Details

The function `model.frame`

is generic.
These functions are method for `model.frame`

for fitted point process models (objects of class `"ppm"`

or `"kppm"`

or `"dppm"`

or `"lppm"`

).

The first argument should be a fitted point process model;
it has to be named `formula`

for consistency with the generic
function.

The result is a data frame containing all the variables used in
fitting the model. The data frame has one row for each quadrature point
used in fitting the model. The quadrature scheme can be extracted using
`quad.ppm`

.

##### Value

A `data.frame`

containing all the variables used in the
fitted model, plus additional variables specified in `…`

.
It has an additional attribute `"terms"`

containing information
about the model formula. For details see `model.frame.glm`

.

##### References

Baddeley, A. and Turner, R. (2000)
Practical maximum pseudolikelihood for spatial point patterns.
*Australian and New Zealand Journal of Statistics*
**42**, 283--322.

##### See Also

##### Examples

```
# NOT RUN {
fit <- ppm(cells ~ x)
mf <- model.frame(fit)
kfit <- kppm(redwood ~ x, "Thomas")
kmf <- model.frame(kfit)
# }
```

*Documentation reproduced from package spatstat, version 1.64-1, License: GPL (>= 2)*