profilepl {spatstat} | R Documentation |
Fits point process models by profile maximum pseudolikelihood
profilepl(s, f, ..., rbord = NULL, verbose = TRUE)
s |
Data frame containing values of the irregular parameters over which the profile pseudolikelihood will be computed. |
f |
Function (such as |
... |
Data passed to |
rbord |
Radius for border correction (same for all models). If omitted, this will be computed from the interactions. |
verbose |
Logical flag indicating whether to print progress reports. |
The model-fitting function ppm
fits point process
models to point pattern data. However,
only the ‘regular’ parameters of the model can be fitted by
ppm
. The model may also depend on ‘irregular’
parameters that must be fixed in any call to ppm
.
This function profilepl
is a wrapper which finds the values of the
irregular parameters that give the best fit. It uses the method of
maximum profile pseudolikelihood.
The argument s
must be a data frame whose columns contain
values of the irregular parameters over which the maximisation is
to be performed.
An irregular parameter may affect either the interpoint interaction or the spatial trend.
in a call to ppm
, the argument interaction
determines the interaction between points. It is usually
a call to a function such as Strauss
. The
arguments of this call are irregular parameters.
For example, the interaction radius parameter r of the Strauss
process, determined by the argument r
to the function Strauss
, is an irregular parameter.
in a call to ppm
, the spatial trend may depend on
covariates, which are supplied by the argument covariates
.
These covariates may be functions written by the user,
of the form function(x,y,...)
, and the extra arguments
...
are irregular parameters.
The argument f
determines the interaction
for each model to be fitted. It would typically be one of the functions
Poisson
,
AreaInter
,
BadGey
,
DiggleGatesStibbard
,
DiggleGratton
,
Fiksel
,
Geyer
,
Hardcore
,
LennardJones
,
OrdThresh
,
Softcore
,
Strauss
or
StraussHard
.
Alternatively it could be a function written by the user.
Columns of s
which match the names of arguments of f
will be interpreted as interaction parameters. Other columns will be
interpreted as trend parameters.
To apply the method of profile maximum pseudolikelihood,
each row of s
will be taken in turn. Interaction parameters in this
row will be passed to f
, resulting in an interaction object.
Then ppm
will be applied to the data ...
using this interaction. Any trend parameters will be passed to
ppm
through the argument covfunargs
.
This results in a fitted point process model.
The value of the log pseudolikelihood from this model is stored.
After all rows of s
have been processed in this way, the
row giving the maximum value of log pseudolikelihood will be found.
The object returned by profilepl
contains the profile
pseudolikelihood function, the best fitting model, and other data.
It can be plotted (yielding a
plot of the log pseudolikelihood values against the irregular
parameters) or printed (yielding information about the best fitting
values of the irregular parameters).
In general, f
may be any function that will return
an interaction object (object of class "interact"
)
that can be used in a call to ppm
. Each argument of
f
must be a single value.
An object of class "profilepl"
. There are methods
for plot
and print
for this class.
The components of the object include
fit |
Best-fitting model |
param |
The data frame |
iopt |
Row index of the best-fitting parameters in |
Adrian Baddeley Adrian.Baddeley@csiro.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner r.turner@auckland.ac.nz
data(cells) # one irregular parameter s <- data.frame(r=seq(0.05,0.15, by=0.01)) ps <- profilepl(s, Strauss, cells) ps if(interactive()) plot(ps) # two irregular parameters s <- expand.grid(r=seq(0.05,0.15, by=0.01),sat=1:3) pg <- profilepl(s, Geyer, cells) pg if(interactive()) plot(pg) ## Not run: pg$fit ## End(Not run) # multitype pattern with a common interaction radius data(betacells) s <- data.frame(R=seq(65,85,by=5)) MS <- function(R) { MultiStrauss(radii=diag(c(R,R))) } pm <- profilepl(s, MS, betacells, ~marks)