Calculate Pseudo-R-Squared for Point Process Model on Linear Network
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.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
X <- rpoislpp(10, simplenet) fit <- lppm(X ~ y) pseudoR2(fit)
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