Extract Design Matrix from Point Process Model
Given a point process model that has been fitted to spatial point pattern data, this function extracts the design matrix of the model.
## S3 method for class 'ppm': model.matrix(object, data=model.frame(object), ..., Q=NULL, keepNA=TRUE)
## S3 method for class 'kppm': model.matrix(object, data=model.frame(object), ..., Q=NULL, keepNA=TRUE)
## S3 method for class 'lppm': model.matrix(object, data=model.frame(object), ..., keepNA=TRUE)
- The fitted point process model. An object of class
- A model frame, containing the data required for the Berman-Turner device.
- A point pattern (class
"ppp") or quadrature scheme (class
"quad") specifying new locations where the covariates should be computed.
- Logical. Determines whether rows containing NA values will be deleted or retained.
- Other arguments (such as
na.action) passed to
These commands are methods for the generic function
model.matrix. They extracts the design matrix of a
spatial point process model (class
More precisely, this command extracts
the design matrix of the generalised linear model associated with
a spatial point process model.
object must be a fitted point process model
(object of class
fitted to spatial point pattern data.
Such objects are produced by the model-fitting
extract 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,
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
which typically delete rows containing
The quadrature points themselves can be extracted using
- A matrix. Columns of the matrix are covariates in the model.
Rows of the matrix correspond to quadrature points
in the fitting procedure (provided
fit <- ppm(cells ~ x) head(model.matrix(fit)) model.matrix(fit, Q=runifpoint(5)) kfit <- kppm(redwood ~ x, "Thomas") m <- model.matrix(kfit)