spatstat.core (version 2.3-1)

summary.ppm: Summarizing a Fitted Point Process Model

Description

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.

Value

summary.ppm returns an object of class "summary.ppm", while print.summary.ppm returns NULL.

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.

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

  # multitype pattern
  # data(demopat)
  # fit <- ppm(demopat, ~marks, Poisson())
  # summary(fit)

  # model with external covariates
  fitX <- ppm(X, ~Z, covariates=list(Z=function(x,y){x+y}))
  summary(fitX)
# }