R: Class of Fitted Point Process Models

ppm.object {spatstat} | R Documentation

## Class of Fitted Point Process Models

### Description

A class `ppm` to represent a fitted stochastic model for a point process. The output of `ppm`.

### Details

An object of class `ppm` represents a stochastic point process model that has been fitted to a point pattern dataset. Typically it is the output of the model fitter, `ppm`.

The class `ppm` has methods for the following standard generic functions:

generic | method | description
`print` | `print.ppm` | print details
`plot` | `plot.ppm` | plot fitted model
`predict` | `predict.ppm` | fitted intensity and conditional intensity
`fitted` | `fitted.ppm` | fitted intensity
`coef` | `coef.ppm` | fitted coefficients of model
`anova` | `anova.ppm` | Analysis of Deviance
`formula` | `formula.ppm` | Extract model formula
`terms` | `terms.ppm` | Terms in the model formula
`labels` | `labels.ppm` | Names of estimable terms in the model formula
`residuals` | `residuals.ppm` | Point process residuals
`simulate` | `simulate.ppm` | Simulate the fitted model
`update` | `update.ppm` | Change or refit the model
`vcov` | `vcov.ppm` | Variance/covariance matrix of parameter estimates
`model.frame` | `model.frame.ppm` | Model frame
`model.matrix` | `model.matrix.ppm` | Design matrix
`logLik` | `logLik.ppm` | log pseudo likelihood
`extractAIC` | `extractAIC.ppm` | pseudolikelihood counterpart of AIC
`nobs` | `nobs.ppm` | number of observations

Objects of class `ppm` can also be handled by the following standard functions, without requiring a special method:

name | description
`confint` | Confidence intervals for parameters
`step` | Stepwise model selection
`drop1` | One-step model improvement
`add1` | One-step model improvement

The class `ppm` also has methods for the following generic functions defined in the spatstat package:

generic | method | description
`as.interact` | `as.interact.ppm` | Interpoint interaction structure
`as.owin` | `as.owin.ppm` | Observation window of data
`berman.test` | `berman.test.ppm` | Berman's test
`envelope` | `envelope.ppm` | Simulation envelopes
`fitin` | `fitin.ppm` | Fitted interaction
`is.marked` | `is.marked.ppm` | Determine whether the model is marked
`is.multitype` | `is.multitype.ppm` | Determine whether the model is multitype
`is.poisson` | `is.poisson.ppm` | Determine whether the model is Poisson
`is.stationary` | `is.stationary.ppm` | Determine whether the model is stationary
`cdf.test` | `cdf.test.ppm` | Spatial distribution test
`quadrat.test` | `quadrat.test.ppm` | Quadrat counting test
`reach` | `reach.ppm` | Interaction range of model
`rmhmodel` | `rmhmodel.ppm` | Model in a form that can be simulated
`rmh` | `rmh.ppm` | Perform simulation
`unitname` | `unitname.ppm` | Name of unit of length

Information about the data (to which the model was fitted) can be extracted using `data.ppm`, `dummy.ppm` and `quad.ppm`.

### Internal format

If you really need to get at the internals, a `ppm` object contains at least the following entries:

`coef` | the fitted regular parameters (as returned by `glm`)
`trend` | the trend formula or `NULL`
`interaction` | the point process interaction family (an object of class `"interact"`) or `NULL`
`Q` | the quadrature scheme used
`maxlogpl` | the maximised value of log pseudolikelihood
`correction` | name of edge correction method used

See `ppm` for explanation of these concepts. The irregular parameters (e.g. the interaction radius of the Strauss process) are encoded in the `interaction` entry. However see the Warnings.

### Warnings

The internal representation of `ppm` objects may change slightly between releases of the spatstat package.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

and Rolf Turner r.turner@auckland.ac.nz

### See Also

`ppm`, `coef.ppm`, `fitted.ppm`, `print.ppm`, `predict.ppm`, `plot.ppm`.

### Examples

      data(cells)
      fit <- ppm(cells, ~ x, Strauss(0.1), correction="periodic")
      fit
      coef(fit)
      ## Not run:
      pred <- predict(fit)

    ## End(Not run)
      pred <- predict(fit, ngrid=20, type="trend")
      ## Not run:
      plot(fit)

    ## End(Not run)

[Package spatstat version 1.54-0 Index]