pixelquad(X, W = as.owin(X))"ppp") containing the
data points for the quadrature scheme."im"),
a window (object of class "owin"), or anything that can
be converted to a window by as.owin."quad" describing the quadrature scheme
(data points, dummy points, and quadrature weights)
suitable as the argument Q of the function ppm() for
fitting a point process model. The quadrature scheme can be inspected using the
print and plot methods for objects
of class "quad".
ppm. It is an alternative to
quadscheme.
The function ppm fits a point process model to an
observed point pattern using
the Berman-Turner quadrature approximation (Berman and Turner, 1992;
Baddeley and Turner, 2000) to the pseudolikelihood of the model.
It requires a quadrature scheme consisting of
the original data point pattern, an additional pattern of dummy points,
and a vector of quadrature weights for all these points.
Such quadrature schemes are represented by objects of class
"quad". See quad.object for a description of this class.Given a grid of pixels, this function creates a quadrature scheme in which there is one dummy point at the centre of each pixel. The counting weights are used (the weight attached to each quadrature point is 1 divided by the number of quadrature points falling in the same pixel).
The argument X specifies the locations of the data points
for the quadrature scheme. Typically this would be a point pattern
dataset.
The argument W specifies the grid of pixels for the dummy
points of the quadrature scheme. It should be a pixel image
(object of class "im"), a window (object of class
"owin"), or anything that can
be converted to a window by as.owin. If W is a
pixel image or a binary mask (a window of type "mask")
then the pixel grid of W will be used. If W is a
rectangular or polygonal window, then it will first be converted to a
binary mask using as.mask at the default pixel
resolution.
quadscheme,
quad.object,
ppmW <- owin(c(0,1),c(0,1))
X <- runifpoint(42, W)
W <- as.mask(W,dimyx=128)
pixelquad(X,W)Run the code above in your browser using DataLab