# pseudoR2.lppm: Calculate Pseudo-R-Squared for Point Process Model on Linear... In spatstat.linnet: Linear Networks Functionality of the 'spatstat' Family

 pseudoR2.lppm R Documentation

## Calculate Pseudo-R-Squared for Point Process Model on Linear Network

### Description

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.

### Usage

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

### Arguments

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

### Value

A single numeric value.

### Author(s)

\spatstatAuthors

.

`pseudoR2`, `deviance.lppm`.
```  X <- rpoislpp(10, simplenet)