`summary`

method for class `"ppm"`

.

```
# S3 method for ppm
summary(object, …, quick=FALSE, fine=FALSE)
# S3 method for summary.ppm
print(x, …)
```

object

A fitted point process model.

…

Ignored.

quick

Logical flag controlling the scope of the summary.

fine

Logical value passed to `vcov.ppm`

determining
whether to compute the quick, coarse estimate of variance
(`fine=FALSE`

, the default) or the slower, finer estimate
(`fine=TRUE`

).

x

Object of class `"summary.ppm"`

as returned by
`summary.ppm`

.

`summary.ppm`

returns an object of class `"summary.ppm"`

,
while `print.summary.ppm`

returns `NULL`

.

This is a method for the generic `summary`

for the class `"ppm"`

. An object of class `"ppm"`

describes a fitted point process model. See `ppm.object`

)
for details of this class.

`summary.ppm`

extracts information about the
type of model that has been fitted, the data to which the model was
fitted, and the values of the fitted coefficients.
(If `quick=TRUE`

then only the information about the type
of model is extracted.)

`print.summary.ppm`

prints this information in a
comprehensible format.

In normal usage, `print.summary.ppm`

is invoked implicitly
when the user calls `summary.ppm`

without assigning its value
to anything. See the examples.

You can also type `coef(summary(object))`

to extract a table
of the fitted coefficients of the point process model `object`

together with standard errors and confidence limits.

# NOT RUN { # invent some data X <- rpoispp(42) # fit a model to it fit <- ppm(X ~ x, Strauss(r=0.1)) # summarize the fitted model summary(fit) # `quick' option summary(fit, quick=TRUE) # coefficients with standard errors and CI coef(summary(fit)) coef(summary(fit, fine=TRUE)) # save the full summary s <- summary(fit) # print it print(s) s # extract stuff names(s) coef(s) s$args$correction s$name s$trend$value # } # NOT RUN { # multitype pattern data(demopat) fit <- ppm(demopat, ~marks, Poisson()) summary(fit) # } # NOT RUN { # model with external covariates fitX <- ppm(X, ~Z, covariates=list(Z=function(x,y){x+y})) summary(fitX) # }