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mpl(Q, trend=~1, interaction=NULL, data, correction="border", rbord=0, use.gam=F)"ppp")
to which the model will be fitted,
or preferably a quadrature scheme (of class "quad")
containing this pattern.~1, indicates the model is stationary
and no trend is to be fitted."interact"
describing the point process interaction
structure, or NULL indicating that a Poisson process (stationary
or nonstationary) should be fitted.Q)."border" indicating the border correction.correction = "border"
this argument specifies the distance by which
the window should be eroded for the border correction.TRUE then computations are performed
using gam instead of glm."ppm" describing a fitted point process
model.
The fitted parameters can be obtained just by printing this object.
Fitted spatial trends can be extracted using the predict method
for this object (see predict.ppm). See ppm.object for details of the format of this object.
mpl()$theta[2]
less than or equal to 0.
The current version of mpl maximises the pseudolikelihood
without constraining the parameters, and does not apply any checks for
sanity after fitting the model.
The trend formula should not use the names
Y, V, W, or SUBSET,
which are reserved
for internal use. The data frame data should have as many rows
as there are points in Q. It should not contain
variables called x, y or marks
as these names are reserved for the Cartesian coordinates
and the marks.
If the model formula involves one of the functions
poly(), bs()
or ns()
(e.g. applied to spatial coordinates x and y),
the fitted coefficients can be misleading.
The resulting fit is not to the raw spatial variates
(x, x^2, x*y, etc.)
but to a transformation of these variates. The transformation is implemented
by poly() in order to achieve better numerical stability.
However the
resulting coefficients are appropriate for use with the transformed
variates, not with the raw variates.
This affects the interpretation of the constant
term in the fitted model, logbeta.
Conventionally, $\beta$ is the background intensity, i.e. the
value taken by the conditional intensity function when all predictors
(including spatial or ``trend'' predictors) are set equal to $0$.
However the coefficient actually produced is the value that the
log conditional intensity takes when all the predictors,
including the transformed
spatial predictors, are set equal to 0, which is not the same thing.Worse still, the result of predict.ppm can be
completely wrong if the trend formula contains one of the
functions poly(), bs()
or ns(). This is a weakness of the underlying
function predict.glm.
If you wish to fit a polynomial trend,
we offer an alternative to poly(),
namely polynom(), which avoids the
difficulty induced by transformations. It is completely analogous
to poly except that it does not orthonormalise.
The resulting coefficient estimates then have
their natural interpretation and can be predicted correctly.
Numerical stability may be compromised.
Q should be either a point pattern
or a quadrature scheme. If it is a point pattern, it is converted
into a quadrature scheme. A quadrature scheme is an object of class "quad"
(see quad.object)
which specifies both the data point pattern and the dummy points
for the quadrature scheme, as well as the quadrature weights
associated with these points.
If Q is simply a point pattern
(of class "ppp", see ppp.object)
then it is interpreted as specifying the
data points only; a set of dummy points specified
by default.dummy() is added, and the default weighting rule is
invoked to compute the quadrature weights.
The usage of mpl() is closely analogous to the Splus/Rfunctions
glm and gam.
The analogy is:
formula trend
family interaction
}
The point process model to be fitted is specified by the
arguments trend and interaction
which are respectively analogous to
the formula and family arguments of glm().
Systematic effects (spatial trend and/or dependence on
spatial covariates) are specified by the argument
trend. This is an Splus/Rformula object, which may be expressed
in terms of the Cartesian coordinates x, y,
the marks marks,
or the variables in the data frame data (if supplied), or both.
It specifies the logarithm of the first order potential
of the process.
The formula should not use the names Y, V, W, or
SUBSET,
which are reserved for internal use.
If trend is absent or equal to the default, ~1, then
the model to be fitted is stationary (or at least, its first order
potential is constant).
Stochastic interactions between random points of the point process
are defined by the argument interaction. This is an object of
class "interact" which is initialised in a very similar way to the
usage of family objects in glm and gam.
See the examples below.
If interaction is missing or NULL, then the model to be fitted
has no interpoint interactions, that is, it is a Poisson process
(stationary or nonstationary according to trend). In this case
the method of maximum pseudolikelihood
coincides with maximum likelihood.
The argument data, if supplied, must be a data frame with
as many rows as there are points in Q.
The $i$th row of data should contain the values of
spatial variables which have been observed
at the $i$th point of Q. In this case Q must be a
quadrature scheme, not merely a point pattern.
Thus, it is not sufficient to have observed
a spatial variable only at the points of the data point pattern;
the variable must also have been observed at certain other
locations in the window.
The variable names x, y and marks
are reserved for the Cartesian
coordinates and the mark values,
and these should not be used for variables in data.
The argument correction is the name of an edge correction method.
The default correction="border" specifies the border correction,
in which the quadrature window (the domain of integration of the
pseudolikelihood) is obtained by trimming off a margin of width rbord
from the observation window of the data pattern.
Not all edge corrections are implemented (or implementable)
for arbitrary windows.
Other options depend on the argument interaction, but these generally
include "periodic" (the periodic or toroidal edge correction
in which opposite edges of a rectangular window are identified)
and "translation"
(the translation correction, see Baddeley 1998 and Baddeley and Turner 2000).
The fitted point process model returned by this function can be printed
(by the print method print.ppm)
to inspect the fitted parameter values.
If a nonparametric spatial trend was fitted, this can be extracted using
the predict method predict.ppm.
This algorithm approximates the log pseudolikelihood
by a sum over a finite set of quadrature points.
Finer quadrature schemes (i.e. those with more
quadrature points) generally yield a better approximation, at the
expense of higher computational load.
Complete control over the quadrature scheme is possible.
See quadscheme for an overview.
Note that the method of maximum pseudolikelihood is believed to be inefficient and biased for point processes with strong interpoint interactions. In such cases, it is advisable to use iterative maximum likelihood methods such as Monte Carlo Maximum Likelihood (Geyer, 1999) provided the appropriate simulation algorithm exists. The maximum pseudolikelihood parameter estimate often serves as a good initial starting point for these iterative methods. Maximum pseudolikelihood may also be used profitably for model selection in the initial phases of modelling.
ppp,
quadscheme,
ppm.object,
Poisson,
Strauss,
StraussHard,
Softcore,
Pairwise,
PairPiece,
Geyer,
Saturated,
OrdThresh,
Ordlibrary(spatstat)
data(nztrees)
Q <- quadscheme(nztrees) # default quadrature scheme
mpl(Q)
# fit the stationary Poisson process
# to point pattern or data/dummy quadrature scheme Q
mpl(Q, ~ x)
# fit the nonstationary Poisson process
# with intensity function lambda(x,y) = exp(a + bx)
# where x,y are the Cartesian coordinates
# and a,b are parameters to be estimated
mpl(Q, ~ polynom(x,2))
# fit the nonstationary Poisson process
# with intensity function lambda(x,y) = exp(a + bx + cx^2)
library(splines)
mpl(Q, ~ bs(x,df=3))
# WARNING: do not use predict.ppm() on this result
# Fits the nonstationary Poisson process
# with intensity function lambda(x,y) = exp(B(x))
# where B is a B-spline with df = 3
mpl(Q, ~1, Strauss(r=0.1), rbord=0.1)
# Fit the stationary Strauss process with interaction range 0.1
# using the border method with margin rbord=0.1
mpl(Q, ~ x, Strauss(0.1), correction="periodic")
# Fit the nonstationary Strauss process with interaction range 0.07
# and exp(first order potential) = activity = beta(x,y) = exp(a+bx)
# using the periodic correction.
data(soilsurvey)
mpl(soilsurvey, ~ bs(pH,3), Strauss(0.1), rbord=0.1, data=soilchem)
# WARNING: do not use predict.ppm() on this result
# Fit the nonstationary Strauss process
# with intensity modelled as a third order spline function of the
# spatial variable "pH" in data frame 'soilchem'
## MULTITYPE POINT PROCESSES ###
data(lansing)
# Multitype point pattern --- trees marked by species
mpl(lansing, ~ marks, Poisson())
# fit stationary marked Poisson process
# with different intensity for each species
mpl(lansing, ~ marks * polynom(x,y,3), Poisson())
# fit nonstationary marked Poisson process
# with different log-cubic trend for each species
<testonly># equivalent functionality - smaller dataset
data(catWaessle)
mpl(catWaessle, ~ marks * polynom(x,y,2), Poisson())</testonly>Run the code above in your browser using DataLab