pseudoR2.lppm | R Documentation |

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

## S3 method for class 'lppm' pseudoR2(object, ..., keepoffset=TRUE)

`object` |
Fitted point process model on a linear network.
An object of class |

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

`...` |
Additional arguments passed to |

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 - D/D0
*

where *D* is the deviance of the fitted model `object`

,
and *D0* 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.

A single numeric value.

.

`pseudoR2`

,
`deviance.lppm`

.

X <- rpoislpp(10, simplenet) fit <- lppm(X ~ y) pseudoR2(fit)

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