# summary.ppm

##### Summarizing a Fitted Point Process Model

`summary`

method for class `"ppm"`

.

##### Usage

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

##### Arguments

- 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`

.

##### Details

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.

##### Value

`summary.ppm`

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

,
while `print.summary.ppm`

returns `NULL`

.

##### Examples

```
# 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)
# }
```

*Documentation reproduced from package spatstat, version 1.49-0, License: GPL (>= 2)*