LennardJones(sigma0=NA)
"interact"
describing the Lennard-Jones interpoint interaction
structure.
d
are rescaled
when fitting the model. Distances are rescaled by dividing by sigma0
.
In the formula for $v(d)$ above,
the interpoint distance $d$ will be replaced by d/sigma0
. The rescaling happens automatically by default.
If the argument sigma0
is missing or NA
(the default),
then sigma0
is taken to be the minimum
nearest-neighbour distance in the data point pattern (in the
call to ppm
). If the argument sigma0
is given, it should be a positive
number, and it should be a rough estimate of the
parameter $sigma$. The ``canonical regular parameters'' estimated by ppm
are
$theta1 = 4 * epsilon * (sigma/sigma0)^12$
and
$theta2 = 4 * epsilon * (sigma/sigma0)^6$.gcontrol=list(maxit=1e3)
in the call to ppm
. Errors are likely to occur if this model is fitted to a point pattern dataset
which does not exhibit both short-range inhibition and
medium-range attraction between points. The values of the parameters
$sigma$ and $epsilon$ may be NA
(because the fitted canonical parameters have opposite sign, which
usually occurs when the pattern is completely random). An absence of warnings does not mean that the fitted model is sensible.
A negative value of $epsilon$ may be obtained (usually when
the pattern is strongly clustered); this does not correspond
to a valid point process model, but the software does not issue a warning. This potential is used
to model interactions between uncharged molecules in statistical physics.
The function ppm()
, which fits point process models to
point pattern data, requires an argument
of class "interact"
describing the interpoint interaction
structure of the model to be fitted.
The appropriate description of the Lennard-Jones pairwise interaction is
yielded by the function LennardJones()
.
See the examples below.
ppm
,
pairwise.family
,
ppm.object
fit <- ppm(cells ~1, LennardJones(), rbord=0.1)
fit
plot(fitin(fit))
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