Description Usage Arguments Details Value Author(s) References See Also Examples
Generates a quadrature scheme (an object of class "quad"
)
from point patterns of data and dummy points.
1  quadscheme(data, dummy, method="grid", ...)

data 
The observed data point pattern.
An object of class 
dummy 
The pattern of dummy points for the quadrature.
An object of class 
method 
The name of the method for calculating quadrature weights: either

... 
Parameters of the weighting method (see below) and parameters for constructing the dummy points if necessary. 
This is the primary method for producing a quadrature schemes
for use by ppm
.
The function ppm
fits a point process model to an
observed point pattern using
the BermanTurner 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.
Quadrature schemes are created by the function
quadscheme
.
The arguments data
and dummy
specify the data and dummy
points, respectively. There is a sensible default for the dummy
points (provided by default.dummy
).
Alternatively the dummy points
may be specified arbitrarily and given in any format recognised by
as.ppp
.
There are also functions for creating dummy patterns
including corners
,
gridcentres
,
stratrand
and
spokes
.
The quadrature region is the region over which we are
integrating, and approximating integrals by finite sums.
If dummy
is a point pattern object (class "ppp"
)
then the quadrature region is taken to be Window(dummy)
.
If dummy
is just a list of x, y coordinates
then the quadrature region defaults to the observation window
of the data pattern, Window(data)
.
If dummy
is missing, then a pattern of dummy points
will be generated using default.dummy
, taking account
of the optional arguments ...
.
By default, the dummy points are arranged in a
rectangular grid; recognised arguments
include nd
(the number of grid points
in the horizontal and vertical directions)
and eps
(the spacing between dummy points).
If random=TRUE
, a systematic random pattern
of dummy points is generated instead.
See default.dummy
for details.
If method = "grid"
then the optional arguments (for ...
) are
(nd, ntile, eps)
.
The quadrature region (defined above) is divided into
an ntile[1]
by ntile[2]
grid of rectangular tiles.
The weight for each
quadrature point is the area of a tile divided by the number of
quadrature points in that tile.
If method="dirichlet"
then the optional arguments are
(exact=TRUE, nd, eps)
.
The quadrature points (both data and dummy) are used to construct the
Dirichlet tessellation. The quadrature weight of each point is the
area of its Dirichlet tile inside the quadrature region.
If exact == TRUE
then this area is computed exactly
using the package deldir
; otherwise it is computed
approximately by discretisation.
An object of class "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"
.
and \rolf
Baddeley, A. and Turner, R. Practical maximum pseudolikelihood for spatial point patterns. Australian and New Zealand Journal of Statistics 42 (2000) 283–322.
Berman, M. and Turner, T.R. Approximating point process likelihoods with GLIM. Applied Statistics 41 (1992) 31–38.
ppm
,
as.ppp
,
quad.object
,
gridweights
,
dirichletWeights
,
corners
,
gridcentres
,
stratrand
,
spokes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  data(simdat)
# grid weights
Q < quadscheme(simdat)
Q < quadscheme(simdat, method="grid")
Q < quadscheme(simdat, eps=0.5) # dummy point spacing 0.5 units
Q < quadscheme(simdat, nd=50) # 1 dummy point per tile
Q < quadscheme(simdat, ntile=25, nd=50) # 4 dummy points per tile
# Dirichlet weights
Q < quadscheme(simdat, method="dirichlet", exact=FALSE)
# random dummy pattern
## Not run:
D < runifpoint(250, Window(simdat))
Q < quadscheme(simdat, D, method="dirichlet", exact=FALSE)
## End(Not run)
# polygonal window
data(demopat)
X < unmark(demopat)
Q < quadscheme(X)
# mask window
Window(X) < as.mask(Window(X))
Q < quadscheme(X)

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