R: Summarizing a Fitted Point Process Model

summary.ppm {spatstat.core} | R Documentation

## Summarizing a Fitted Point Process Model

### Description

`summary` method for class `"ppm"`.

### Usage

      ## S3 method for class 'ppm'
    summary(object, ..., quick=FALSE, fine=FALSE)
      ## S3 method for class '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`.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

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

### Examples

      # 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)

[Package spatstat.core version 1.65-5 Index]