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 ppm
model.matrix(object,
data=model.frame(object, na.action=NULL),
…,
Q=NULL, keepNA=TRUE) # S3 method for kppm
model.matrix(object,
data=model.frame(object, na.action=NULL),
…,
Q=NULL, keepNA=TRUE)
# S3 method for dppm
model.matrix(object,
data=model.frame(object, na.action=NULL),
…,
Q=NULL, keepNA=TRUE)
# S3 method for lppm
model.matrix(object,
data=model.frame(object, na.action=NULL),
…,
keepNA=TRUE)
# S3 method for ippm
model.matrix(object,
data=model.frame(object, na.action=NULL),
…,
Q=NULL, keepNA=TRUE,
irregular=FALSE)
The fitted point process model. An object of class "ppm"
or "kppm"
or "dppm"
or "ippm"
or "lppm"
.
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
model.matrix.lm
.
Logical value indicating whether to include the irregular score components.
A matrix. Columns of the matrix are canonical covariates in the model.
Rows of the matrix correspond to quadrature points
in the fitting procedure (provided keepNA=TRUE
).
These commands are methods for the generic function
model.matrix
.
They extract the design matrix of a spatial point process model
(class "ppm"
or "kppm"
or "dppm"
or "lppm"
).
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"
or "kppm"
or "dppm"
or "lppm"
)
fitted to spatial point pattern data.
Such objects are produced by the model-fitting
functions ppm
, kppm
, dppm
and lppm
.
The methods model.matrix.ppm
,
model.matrix.kppm
, model.matrix.dppm
and model.matrix.lppm
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,
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
.
model.matrix
,
model.images
,
ppm
,
kppm
,
dppm
,
lppm
,
ippm
,
ppm.object
,
quad.ppm
,
residuals.ppm
# NOT RUN {
fit <- ppm(cells ~ x)
head(model.matrix(fit))
model.matrix(fit, Q=runifpoint(5))
kfit <- kppm(redwood ~ x, "Thomas")
m <- model.matrix(kfit)
# }
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