# pseudoR2

##### Calculate Pseudo-R-Squared for Point Process Model

Given a fitted point process model, calculate the pseudo-R-squared value, which measures the fraction of variation in the data that is explained by the model.

##### Usage

`pseudoR2(object, …)` # S3 method for ppm
pseudoR2(object, …, keepoffset=TRUE)

# S3 method for lppm
pseudoR2(object, …, keepoffset=TRUE)

##### Arguments

- object
Fitted point process model. An object of class

`"ppm"`

or`"lppm"`

.- keepoffset
Logical value indicating whether to retain offset terms in the model when computing the deviance difference. See Details.

- …
Additional arguments passed to

`deviance.ppm`

or`deviance.lppm`

.

##### Details

The function `pseudoR2`

is generic, with methods
for fitted point process models of class `"ppm"`

and
`"lppm"`

.

This function computes McFadden's pseudo-Rsquared
$$
R^2 = 1 - \frac{D}{D_0}
$$
where \(D\) is the deviance of the fitted model `object`

,
and \(D_0\) is the deviance of the null model.
Deviance is defined as twice the negative log-likelihood
or log-pseudolikelihood.

The null model is usually obtained by re-fitting the model
using the trend formula `~1`

.
However if the original model formula included `offset`

terms,
and if `keepoffset=TRUE`

(the default),
then the null model formula consists of these offset terms. This
ensures that the `pseudoR2`

value is non-negative.

##### Value

A single numeric value.

##### See Also

##### Examples

```
# NOT RUN {
fit <- ppm(swedishpines ~ x+y)
pseudoR2(fit)
xcoord <- as.im(function(x,y) x, Window(swedishpines))
fut <- ppm(swedishpines ~ offset(xcoord/200) + y)
pseudoR2(fut)
# }
```

*Documentation reproduced from package spatstat, version 1.59-0, License: GPL (>= 2)*