spatstat (version 1.15-3)

model.images: Compute Images of Constructed Covariates

Description

For a point process model fitted to spatial point pattern data, this function computes pixel images of the covariates in the design matrix.

Usage

model.images(object, W = as.owin(object), ...)

Arguments

object

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

W

A window (object of class "owin") in which the images should be computed. Defaults to the window in which the model was fitted.

...

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

Value

• An object of class "listof" consisting of a named list of pixel images (objects of class "im").

Details

This command is similar to model.matrix.ppm except that it computes pixel images of the covariates, instead of computing the covariate values at certain points only.

The object must be a fitted spatial point process model (object of class "ppm") produced by the model-fitting function ppm.

The spatial covariates required by the model-fitting procedure are computed at every pixel location in the window W.

Note that the spatial covariates computed here are not the original covariates that were supplied when fitting the model. Rather, they are the covariates that actually appear in the loglinear representation of the (conditional) intensity and in the columns of the design matrix. For example, they might include dummy or indicator variables for different levels of a factor, depending on the contrasts that are in force.

The pixel resolution is determined by W if W is a mask (that is W$type = "mask"). Otherwise, the pixel resolution is determined by spatstat.options.

The result is a named list of pixel images (objects of class "im") containing the values of the spatial covariates. The names of the list elements are the names of the covariates determined by model.matrix.lm. The result is also of class "listof" so that it can be plotted immediately.

See Also

model.matrix.ppm, ppm, ppm.object, im, im.object, plot.listof, spatstat.options

Examples

data(cells)
   fit <- ppm(cells, ~x)
   model.images(fit)
   fit2 <- ppm(cells, ~cut(x,3))
   model.images(fit2)