# ppm.object

##### Class of Fitted Point Process Models

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:

`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:

`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

`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

##### 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)
pred <- predict(fit)
pred <- predict(fit, ngrid=20, type="trend")
plot(fit)
```

*Documentation reproduced from package spatstat, version 1.41-1, License: GPL (>= 2)*