# subfits

##### Extract List of Individual Point Process Models

Takes a Gibbs point process model that has been fitted to several point patterns simultaneously, and produces a list of fitted point process models for the individual point patterns.

##### Usage

```
subfits(object, what="models", verbose=FALSE)
subfits.old(object, what="models", verbose=FALSE)
subfits.new(object, what="models", verbose=FALSE)
```

##### Arguments

- object
An object of class

`"mppm"`

representing a point process model fitted to several point patterns.- what
What should be returned. Either

`"models"`

to return the fitted models, or`"interactions"`

to return the fitted interactions only.- verbose
Logical flag indicating whether to print progress reports.

##### Details

`object`

is assumed to have been generated by
`mppm`

. It represents a point process model that has been
fitted to a list of several point patterns, with covariate data.

For each of the *individual* point pattern
datasets, this function derives the corresponding fitted model
for that dataset only (i.e. a point process model for the \(i\)th
point pattern, that is consistent with `object`

).

If `what="models"`

,
the result is a list of point process models (a list of objects of class
`"ppm"`

), one model for each point pattern dataset in the
original fit.
If `what="interactions"`

,
the result is a list of fitted interpoint interactions (a list of
objects of class
`"fii"`

).

Two different algorithms are provided, as
`subfits.old`

and `subfits.new`

.
Currently `subfits`

is the same as the old algorithm
`subfits.old`

because the newer algorithm is too memory-hungry.

##### Value

A list of point process models (a list of objects of class
`"ppm"`

) or a list of fitted interpoint interactions (a list of
objects of class `"fii"`

).

##### References

Baddeley, A., Rubak, E. and Turner, R. (2015)
*Spatial Point Patterns: Methodology and Applications with R*.
London: Chapman and Hall/CRC Press.

##### See Also

##### Examples

```
# NOT RUN {
H <- hyperframe(Wat=waterstriders)
fit <- mppm(Wat~x, data=H)
subfits(fit)
H$Wat[[3]] <- rthin(H$Wat[[3]], 0.1)
fit2 <- mppm(Wat~x, data=H, random=~1|id)
subfits(fit2)
# }
```

*Documentation reproduced from package spatstat, version 1.49-0, License: GPL (>= 2)*