spatstat (version 1.20-2)

model.matrix.ppm: Extract Design Matrix from Point Process Model

Description

Given a point process model that has been fitted to spatial point pattern data, this function extracts the design matrix of the model.

Usage

## S3 method for class 'ppm':
model.matrix(object, data=model.frame(object), ..., keepNA=TRUE)

Arguments

object

The fitted point process model (an object of class "ppm".)

data

A model frame, containing the data required for the Berman-Turner device.

keepNA

Logical. Determines whether rows containing NA values will be deleted or retained.

...

Other arguments (such as na.action) passed to model.matrix.lm.

Value

• A matrix. Rows of the matrix correspond to quadrature points in the fitting procedure (provided keepNA=TRUE). Columns are covariates in the model.

Details

This command is a method for the generic function model.matrix. It extracts the design matrix of a spatial point process model (class "ppm").

More precisely, this command extracts the design matrix of the generalised linear model associated with a spatial point process model. The object must be a fitted point process model (object of class "ppm") fitted to spatial point pattern data. Such objects are produced by the model-fitting function ppm.

This function model.matrix.ppm extracts the model matrix for the GLM.

The result is a matrix, with one row for every quadrature point in the fitting procedure, and one column for every constructed covariate in the design matrix.

If there are NA values in the covariates, the argument keepNA determines whether to retain or delete the corresponding rows of the model matrix. The default keepNA=TRUE is to retain them. Note that this differs from the default behaviour of many other methods for model.matrix, which typically delete rows containing NA. The quadrature points themselves can be extracted using quad.ppm.

See Also

model.images, ppm, ppm.object, quad.ppm, residuals.ppm, model.matrix

Examples

data(cells)
   fit <- ppm(cells, ~x)
   model.matrix(fit)
   # matrix with two columns: '(Intercept)' and 'x'