https://github.com/cran/spatstat
Tip revision: 94dac762f5b1607c843aa97ea6a6caa68343fdb1 authored by Adrian Baddeley on 22 March 2019, 12:40:03 UTC
version 1.59-0
version 1.59-0
Tip revision: 94dac76
envelope.Rd
\name{envelope}
\alias{envelope}
\alias{envelope.ppp}
\alias{envelope.ppm}
\alias{envelope.kppm}
\title{Simulation Envelopes of Summary Function}
\description{
Computes simulation envelopes of a summary function.
}
\usage{
envelope(Y, fun, \dots)
\method{envelope}{ppp}(Y, fun=Kest, nsim=99, nrank=1, \dots,
funargs=list(), funYargs=funargs,
simulate=NULL, fix.n=FALSE, fix.marks=FALSE,
verbose=TRUE, clipdata=TRUE,
transform=NULL, global=FALSE, ginterval=NULL, use.theory=NULL,
alternative=c("two.sided", "less", "greater"),
scale=NULL, clamp=FALSE,
savefuns=FALSE, savepatterns=FALSE,
nsim2=nsim, VARIANCE=FALSE, nSD=2, Yname=NULL, maxnerr=nsim,
do.pwrong=FALSE, envir.simul=NULL)
\method{envelope}{ppm}(Y, fun=Kest, nsim=99, nrank=1, \dots,
funargs=list(), funYargs=funargs,
simulate=NULL, fix.n=FALSE, fix.marks=FALSE,
verbose=TRUE, clipdata=TRUE,
start=NULL, control=update(default.rmhcontrol(Y), nrep=nrep), nrep=1e5,
transform=NULL, global=FALSE, ginterval=NULL, use.theory=NULL,
alternative=c("two.sided", "less", "greater"),
scale=NULL, clamp=FALSE,
savefuns=FALSE, savepatterns=FALSE,
nsim2=nsim, VARIANCE=FALSE, nSD=2, Yname=NULL, maxnerr=nsim,
do.pwrong=FALSE, envir.simul=NULL)
\method{envelope}{kppm}(Y, fun=Kest, nsim=99, nrank=1, \dots,
funargs=list(), funYargs=funargs,
simulate=NULL,
verbose=TRUE, clipdata=TRUE,
transform=NULL, global=FALSE, ginterval=NULL, use.theory=NULL,
alternative=c("two.sided", "less", "greater"),
scale=NULL, clamp=FALSE,
savefuns=FALSE, savepatterns=FALSE,
nsim2=nsim, VARIANCE=FALSE, nSD=2, Yname=NULL, maxnerr=nsim,
do.pwrong=FALSE, envir.simul=NULL)
}
\arguments{
\item{Y}{
Object containing point pattern data.
A point pattern (object of class
\code{"ppp"}) or a fitted point process model
(object of class \code{"ppm"} or \code{"kppm"}).
}
\item{fun}{
Function that computes the desired summary statistic
for a point pattern.
}
\item{nsim}{
Number of simulated point patterns to be generated
when computing the envelopes.
}
\item{nrank}{
Integer. Rank of the envelope value amongst the \code{nsim} simulated
values. A rank of 1 means that the minimum and maximum
simulated values will be used.
}
\item{\dots}{
Extra arguments passed to \code{fun}.
}
\item{funargs}{
A list, containing extra arguments to be passed to \code{fun}.
}
\item{funYargs}{
Optional. A list, containing extra arguments to be passed to
\code{fun} when applied to the original data \code{Y} only.
}
\item{simulate}{
Optional. Specifies how to generate the simulated point patterns.
If \code{simulate} is an expression in the R language, then this
expression will be evaluated \code{nsim} times,
to obtain \code{nsim} point patterns which are taken as the
simulated patterns from which the envelopes are computed.
If \code{simulate} is a function, then this function will be
repeatedly applied to the data pattern \code{Y} to obtain
\code{nsim} simulated patterns.
If \code{simulate} is a list of point patterns, then the entries
in this list will be treated as the simulated patterns from which
the envelopes are computed.
Alternatively \code{simulate} may be an object produced by the
\code{envelope} command: see Details.
}
\item{fix.n}{
Logical. If \code{TRUE}, simulated patterns will have the
same number of points as the original data pattern.
This option is currently not available for \code{envelope.kppm}.
}
\item{fix.marks}{
Logical. If \code{TRUE}, simulated patterns will have the
same number of points \emph{and} the same marks as the
original data pattern. In a multitype point pattern this means that
the simulated patterns will have the same number of points
\emph{of each type} as the original data.
This option is currently not available for \code{envelope.kppm}.
}
\item{verbose}{
Logical flag indicating whether to print progress reports
during the simulations.
}
\item{clipdata}{
Logical flag indicating whether the data point pattern should be
clipped to the same window as the simulated patterns,
before the summary function for the data is computed.
This should usually be \code{TRUE} to ensure that the
data and simulations are properly comparable.
}
\item{start,control}{
Optional. These specify the arguments \code{start} and \code{control}
of \code{rmh}, giving complete control over the simulation
algorithm. Applicable only when \code{Y} is a fitted model
of class \code{"ppm"}.
}
\item{nrep}{
Number of iterations in the Metropolis-Hastings simulation
algorithm. Applicable only when \code{Y} is a fitted model
of class \code{"ppm"}.
}
\item{transform}{
Optional. A transformation to be applied to the
function values, before the envelopes are computed.
An expression object (see Details).
}
\item{global}{
Logical flag indicating whether envelopes should be pointwise
(\code{global=FALSE}) or simultaneous (\code{global=TRUE}).
}
\item{ginterval}{
Optional.
A vector of length 2 specifying
the interval of \eqn{r} values for the simultaneous critical
envelopes. Only relevant if \code{global=TRUE}.
}
\item{use.theory}{
Logical value indicating whether to use the theoretical value,
computed by \code{fun}, as the reference value for simultaneous
envelopes. Applicable only when \code{global=TRUE}.
Default is \code{use.theory=TRUE} if \code{Y} is a point pattern,
or a point process model equivalent to Complete Spatial Randomness,
and \code{use.theory=FALSE} otherwise.
}
\item{alternative}{
Character string determining whether the envelope corresponds
to a two-sided test (\code{side="two.sided"}, the default)
or a one-sided test with a lower critical boundary
(\code{side="less"}) or a one-sided test
with an upper critical boundary (\code{side="greater"}).
}
\item{scale}{
Optional. Scaling function for global envelopes.
A function in the \R language which determines the
relative scale of deviations, as a function of
distance \eqn{r}, when computing the global envelopes.
Applicable only when \code{global=TRUE}.
Summary function values for distance \code{r}
will be \emph{divided} by \code{scale(r)} before the
maximum deviation is computed. The resulting global envelopes
will have width proportional to \code{scale(r)}.
}
\item{clamp}{
Logical value indicating how to compute envelopes when
\code{alternative="less"} or \code{alternative="greater"}.
Deviations of the observed
summary function from the theoretical summary function are initially
evaluated as signed real numbers, with large positive values indicating
consistency with the alternative hypothesis.
If \code{clamp=FALSE} (the default), these values are not changed.
If \code{clamp=TRUE}, any negative values are replaced by zero.
}
\item{savefuns}{
Logical flag indicating whether to save all the simulated
function values.
}
\item{savepatterns}{
Logical flag indicating whether to save all the simulated
point patterns.
}
\item{nsim2}{
Number of extra simulated point patterns to be generated
if it is necessary to use simulation to estimate the theoretical
mean of the summary function. Only relevant when \code{global=TRUE}
and the simulations are not based on CSR.
}
\item{VARIANCE}{
Logical. If \code{TRUE}, critical envelopes will be calculated
as sample mean plus or minus \code{nSD} times sample standard
deviation.
}
\item{nSD}{
Number of estimated standard deviations used to determine
the critical envelopes, if \code{VARIANCE=TRUE}.
}
\item{Yname}{
Character string that should be used as the name of the
data point pattern \code{Y} when printing or plotting the results.
}
\item{maxnerr}{
Maximum number of rejected patterns.
If \code{fun} yields an error when applied to a simulated point
pattern (for example, because the pattern is empty and \code{fun}
requires at least one point), the pattern will be rejected
and a new random point pattern will be generated. If this happens
more than \code{maxnerr} times, the algorithm will give up.
}
\item{do.pwrong}{
Logical. If \code{TRUE}, the algorithm will also estimate
the true significance level of the \dQuote{wrong} test (the test that
declares the summary function for the data to be significant
if it lies outside the \emph{pointwise} critical boundary at any
point). This estimate is printed when the result is printed.
}
\item{envir.simul}{
Environment in which to evaluate the expression \code{simulate},
if not the current environment.
}
}
\value{
An object of class \code{"envelope"}
and \code{"fv"}, see \code{\link{fv.object}},
which can be printed and plotted directly.
Essentially a data frame containing columns
\item{r}{the vector of values of the argument \eqn{r}
at which the summary function \code{fun} has been estimated
}
\item{obs}{
values of the summary function for the data point pattern
}
\item{lo}{
lower envelope of simulations
}
\item{hi}{
upper envelope of simulations
}
and \emph{either}
\item{theo}{
theoretical value of the summary function under CSR
(Complete Spatial Randomness, a uniform Poisson point process)
if the simulations were generated according to CSR
}
\item{mmean}{
estimated theoretical value of the summary function,
computed by averaging simulated values,
if the simulations were not generated according to CSR.
}
Additionally, if \code{savepatterns=TRUE}, the return value has an attribute
\code{"simpatterns"} which is a list containing the \code{nsim}
simulated patterns. If \code{savefuns=TRUE}, the return value
has an attribute \code{"simfuns"} which is an object of class
\code{"fv"} containing the summary functions
computed for each of the \code{nsim} simulated patterns.
}
\details{
The \code{envelope} command performs simulations and
computes envelopes of a summary statistic based on the simulations.
The result is an object that can be plotted to display the envelopes.
The envelopes can be used to assess the goodness-of-fit of
a point process model to point pattern data.
For the most basic use, if you have a point pattern \code{X} and
you want to test Complete Spatial Randomness (CSR), type
\code{plot(envelope(X, Kest,nsim=39))} to see the \eqn{K} function
for \code{X} plotted together with the envelopes of the
\eqn{K} function for 39 simulations of CSR.
The \code{envelope} function is generic, with methods for
the classes \code{"ppp"}, \code{"ppm"} and \code{"kppm"}
described here. There are also methods for the classes \code{"pp3"},
\code{"lpp"} and \code{"lppm"} which are described separately
under \code{\link{envelope.pp3}} and \code{\link{envelope.lpp}}.
Envelopes can also be computed from other envelopes, using
\code{\link{envelope.envelope}}.
To create simulation envelopes, the command \code{envelope(Y, ...)}
first generates \code{nsim} random point patterns
in one of the following ways.
\itemize{
\item
If \code{Y} is a point pattern (an object of class \code{"ppp"})
and \code{simulate=NULL},
then we generate \code{nsim} simulations of
Complete Spatial Randomness (i.e. \code{nsim} simulated point patterns
each being a realisation of the uniform Poisson point process)
with the same intensity as the pattern \code{Y}.
(If \code{Y} is a multitype point pattern, then the simulated patterns
are also given independent random marks; the probability
distribution of the random marks is determined by the
relative frequencies of marks in \code{Y}.)
\item
If \code{Y} is a fitted point process model (an object of class
\code{"ppm"} or \code{"kppm"}) and \code{simulate=NULL},
then this routine generates \code{nsim} simulated
realisations of that model.
\item
If \code{simulate} is supplied, then it determines how the
simulated point patterns are generated. It may be either
\itemize{
\item
an expression in the R language, typically containing a call
to a random generator. This expression will be evaluated
\code{nsim} times to yield \code{nsim} point patterns. For example
if \code{simulate=expression(runifpoint(100))} then each simulated
pattern consists of exactly 100 independent uniform random points.
\item
a function in the R language, typically containing a call to a
random generator. This function will be applied repeatedly
to the original data pattern \code{Y} to yield \code{nsim} point
patterns. For example if \code{simulate=\link{rlabel}} then each
simulated pattern was generated by evaluating \code{\link{rlabel}(Y)}
and consists of a randomly-relabelled version of \code{Y}.
\item
a list of point patterns.
The entries in this list will be taken as the simulated patterns.
\item
an object of class \code{"envelope"}. This should have been
produced by calling \code{envelope} with the
argument \code{savepatterns=TRUE}.
The simulated point patterns that were saved in this object
will be extracted and used as the simulated patterns for the
new envelope computation. This makes it possible to plot envelopes
for two different summary functions based on exactly the same set of
simulated point patterns.
}
}
The summary statistic \code{fun} is applied to each of these simulated
patterns. Typically \code{fun} is one of the functions
\code{Kest}, \code{Gest}, \code{Fest}, \code{Jest}, \code{pcf},
\code{Kcross}, \code{Kdot}, \code{Gcross}, \code{Gdot},
\code{Jcross}, \code{Jdot}, \code{Kmulti}, \code{Gmulti},
\code{Jmulti} or \code{Kinhom}. It may also be a character string
containing the name of one of these functions.
The statistic \code{fun} can also be a user-supplied function;
if so, then it must have arguments \code{X} and \code{r}
like those in the functions listed above, and it must return an object
of class \code{"fv"}.
Upper and lower critical envelopes are computed in one of the following ways:
\describe{
\item{pointwise:}{by default, envelopes are calculated pointwise
(i.e. for each value of the distance argument \eqn{r}), by sorting the
\code{nsim} simulated values, and taking the \code{m}-th lowest
and \code{m}-th highest values, where \code{m = nrank}.
For example if \code{nrank=1}, the upper and lower envelopes
are the pointwise maximum and minimum of the simulated values.
The pointwise envelopes are \bold{not} \dQuote{confidence bands}
for the true value of the function! Rather,
they specify the critical points for a Monte Carlo test
(Ripley, 1981). The test is constructed by choosing a
\emph{fixed} value of \eqn{r}, and rejecting the null hypothesis if the
observed function value
lies outside the envelope \emph{at this value of} \eqn{r}.
This test has exact significance level
\code{alpha = 2 * nrank/(1 + nsim)}.
}
\item{simultaneous:}{if \code{global=TRUE}, then the envelopes are
determined as follows. First we calculate the theoretical mean value of
the summary statistic (if we are testing CSR, the theoretical
value is supplied by \code{fun}; otherwise we perform a separate
set of \code{nsim2} simulations, compute the
average of all these simulated values, and take this average
as an estimate of the theoretical mean value). Then, for each simulation,
we compare the simulated curve to the theoretical curve, and compute the
maximum absolute difference between them (over the interval
of \eqn{r} values specified by \code{ginterval}). This gives a
deviation value \eqn{d_i}{d[i]} for each of the \code{nsim}
simulations. Finally we take the \code{m}-th largest of the
deviation values, where \code{m=nrank}, and call this
\code{dcrit}. Then the simultaneous envelopes are of the form
\code{lo = expected - dcrit} and \code{hi = expected + dcrit} where
\code{expected} is either the theoretical mean value \code{theo}
(if we are testing CSR) or the estimated theoretical value
\code{mmean} (if we are testing another model). The simultaneous critical
envelopes have constant width \code{2 * dcrit}.
The simultaneous critical envelopes allow us to perform a different
Monte Carlo test (Ripley, 1981). The test rejects the null
hypothesis if the graph of the observed function
lies outside the envelope \bold{at any value of} \eqn{r}.
This test has exact significance level
\code{alpha = nrank/(1 + nsim)}.
This test can also be performed using \code{\link{mad.test}}.
}
\item{based on sample moments:}{if \code{VARIANCE=TRUE},
the algorithm calculates the
(pointwise) sample mean and sample variance of
the simulated functions. Then the envelopes are computed
as mean plus or minus \code{nSD} standard deviations.
These envelopes do not have an exact significance interpretation.
They are a naive approximation to
the critical points of the Neyman-Pearson test
assuming the summary statistic is approximately Normally
distributed.
}
}
The return value is an object of class \code{"fv"} containing
the summary function for the data point pattern,
the upper and lower simulation envelopes, and
the theoretical expected value (exact or estimated) of the summary function
for the model being tested. It can be plotted
using \code{\link{plot.envelope}}.
If \code{VARIANCE=TRUE} then the return value also includes the
sample mean, sample variance and other quantities.
Arguments can be passed to the function \code{fun} through
\code{...}. This means that you simply specify these arguments in the call to
\code{envelope}, and they will be passed to \code{fun}.
In particular, the argument \code{correction}
determines the edge correction to be used to calculate the summary
statistic. See the section on Edge Corrections, and the Examples.
Arguments can also be passed to the function \code{fun}
through the list \code{funargs}. This mechanism is typically used if
an argument of \code{fun} has the same name as an argument of
\code{envelope}. The list \code{funargs} should contain
entries of the form \code{name=value}, where each \code{name} is the name
of an argument of \code{fun}.
There is also an option, rarely used, in which different function
arguments are used when computing the summary function
for the data \code{Y} and for the simulated patterns.
If \code{funYargs} is given, it will be used
when the summary function for the data \code{Y} is computed,
while \code{funargs} will be used when computing the summary function
for the simulated patterns.
This option is only needed in rare cases: usually the basic principle
requires that the data and simulated patterns must be treated
equally, so that \code{funargs} and \code{funYargs} should be identical.
If \code{Y} is a fitted cluster point process model (object of
class \code{"kppm"}), and \code{simulate=NULL},
then the model is simulated directly
using \code{\link{simulate.kppm}}.
If \code{Y} is a fitted Gibbs point process model (object of
class \code{"ppm"}), and \code{simulate=NULL},
then the model is simulated
by running the Metropolis-Hastings algorithm \code{\link{rmh}}.
Complete control over this algorithm is provided by the
arguments \code{start} and \code{control} which are passed
to \code{\link{rmh}}.
For simultaneous critical envelopes (\code{global=TRUE})
the following options are also useful:
\describe{
\item{\code{ginterval}}{determines the interval of \eqn{r} values
over which the deviation between curves is calculated.
It should be a numeric vector of length 2.
There is a sensible default (namely, the recommended plotting
interval for \code{fun(X)}, or the range of \code{r} values if
\code{r} is explicitly specified).
}
\item{\code{transform}}{specifies a transformation of the
summary function \code{fun} that will be carried out before the
deviations are computed.
Such transforms are useful if \code{global=TRUE} or
\code{VARIANCE=TRUE}.
The \code{transform} must be an expression object
using the symbol \code{.} to represent the function value
(and possibly other symbols recognised by \code{\link{with.fv}}).
For example,
the conventional way to normalise the \eqn{K} function
(Ripley, 1981) is to transform it to the \eqn{L} function
\eqn{L(r) = \sqrt{K(r)/\pi}}{L(r) = sqrt(K(r)/\pi)}
and this is implemented by setting
\code{transform=expression(sqrt(./pi))}.
}
}
It is also possible to extract the summary functions for each of the
individual simulated point patterns, by setting \code{savefuns=TRUE}.
Then the return value also
has an attribute \code{"simfuns"} containing all the
summary functions for the individual simulated patterns.
It is an \code{"fv"} object containing
functions named \code{sim1, sim2, ...} representing the \code{nsim}
summary functions.
It is also possible to save the simulated point patterns themselves,
by setting \code{savepatterns=TRUE}. Then the return value also has
an attribute \code{"simpatterns"} which is a list of length
\code{nsim} containing all the simulated point patterns.
See \code{\link{plot.envelope}} and \code{\link{plot.fv}}
for information about how to plot the envelopes.
Different envelopes can be recomputed from the same data
using \code{\link{envelope.envelope}}.
Envelopes can be combined using \code{\link{pool.envelope}}.
}
\section{Errors and warnings}{
An error may be generated if one of the simulations produces a
point pattern that is empty, or is otherwise unacceptable to the
function \code{fun}.
The upper envelope may be \code{NA} (plotted as plus or minus
infinity) if some of the function values
computed for the simulated point patterns are \code{NA}.
Whether this occurs will depend on the function \code{fun},
but it usually happens when the simulated point pattern does not contain
enough points to compute a meaningful value.
}
\section{Confidence intervals}{
Simulation envelopes do \bold{not} compute confidence intervals;
they generate significance bands.
If you really need a confidence interval for the true summary function
of the point process, use \code{\link{lohboot}}.
See also \code{\link{varblock}}.
}
\section{Edge corrections}{
It is common to apply a correction for edge effects when
calculating a summary function such as the \eqn{K} function.
Typically the user has a choice between several possible edge
corrections.
In a call to \code{envelope}, the user can specify the edge correction
to be applied in \code{fun}, using the argument \code{correction}.
See the Examples below.
\describe{
\item{Summary functions in \pkg{spatstat}}{
Summary functions that are available in \pkg{spatstat}, such as
\code{\link{Kest}}, \code{\link{Gest}} and \code{\link{pcf}},
have a standard argument called \code{correction} which specifies
the name of one or more edge corrections.
The list of available edge
corrections is different for each summary function,
and may also depend on the kind of window in which the point pattern is
recorded.
In the
case of \code{Kest} (the default and most frequently used value of
\code{fun}) the best edge correction is Ripley's isotropic
correction if the window is rectangular or polygonal,
and the translation correction if the window is a binary mask.
See the help files for the individual
functions for more information.
All the summary functions in \pkg{spatstat}
recognise the option \code{correction="best"}
which gives the \dQuote{best} (most accurate) available edge correction
for that function.
In a call to \code{envelope}, if \code{fun} is one of the
summary functions provided in \pkg{spatstat}, then the default
is \code{correction="best"}. This means that
\emph{by default, the envelope will be computed
using the \dQuote{best} available edge correction}.
The user can override this default by specifying the argument
\code{correction}. For example the computation can be accelerated
by choosing another edge correction which is less accurate
than the \dQuote{best} one, but faster to compute.
}
\item{User-written summary functions}{
If \code{fun} is a function written by the user,
then \code{envelope} has to guess what to do.
If \code{fun} has an argument
called \code{correction}, or has \code{\dots} arguments,
then \code{envelope} assumes that the function
can handle a correction argument. To compute the envelope,
\code{fun} will be called with a \code{correction} argument.
The default is \code{correction="best"}, unless
overridden in the call to \code{envelope}.
Otherwise, if \code{fun} does not have an argument
called \code{correction} and does not have \code{\dots} arguments,
then \code{envelope} assumes that the function
\emph{cannot} handle a correction argument. To compute the
envelope, \code{fun} is called without a correction argument.
}
}
}
\references{
Baddeley, A., Diggle, P.J., Hardegen, A., Lawrence, T., Milne,
R.K. and Nair, G. (2014) On tests of spatial pattern based on
simulation envelopes. \emph{Ecological Monographs} \bold{84} (3) 477--489.
Cressie, N.A.C. \emph{Statistics for spatial data}.
John Wiley and Sons, 1991.
Diggle, P.J. \emph{Statistical analysis of spatial point patterns}.
Arnold, 2003.
Ripley, B.D. (1981)
\emph{Spatial statistics}.
John Wiley and Sons.
Ripley, B.D. \emph{Statistical inference for spatial processes}.
Cambridge University Press, 1988.
Stoyan, D. and Stoyan, H. (1994)
Fractals, random shapes and point fields:
methods of geometrical statistics.
John Wiley and Sons.
}
\seealso{
\code{\link{dclf.test}},
\code{\link{mad.test}}
for envelope-based tests.
\code{\link{fv.object}},
\code{\link{plot.envelope}},
\code{\link{plot.fv}},
\code{\link{envelope.envelope}},
\code{\link{pool.envelope}}
for handling envelopes.
There are also methods for \code{print} and \code{summary}.
\code{\link{Kest}},
\code{\link{Gest}},
\code{\link{Fest}},
\code{\link{Jest}},
\code{\link{pcf}},
\code{\link{ppp}},
\code{\link{ppm}},
\code{\link{default.expand}}
}
\examples{
X <- simdat
# Envelope of K function under CSR
\dontrun{
plot(envelope(X))
}
\testonly{
plot(envelope(X, nsim=3))
}
# Translation edge correction (this is also FASTER):
\dontrun{
plot(envelope(X, correction="translate"))
}
\testonly{
E <- envelope(X, nsim=3, correction="translate")
}
# Global envelopes
\dontrun{
plot(envelope(X, Lest, global=TRUE))
plot(envelope(X, Kest, global=TRUE, scale=function(r) { r }))
}
\testonly{
E <- envelope(X, Lest, nsim=3, global=TRUE)
E <- envelope(X, Kest, nsim=3, global=TRUE, scale=function(r) { r })
E
summary(E)
}
# Envelope of K function for simulations from Gibbs model
\dontrun{
fit <- ppm(cells ~1, Strauss(0.05))
plot(envelope(fit))
plot(envelope(fit), global=TRUE)
}
\testonly{
fit <- ppm(cells ~1, Strauss(0.05), nd=20)
E <- envelope(fit, nsim=3, correction="border", nrep=100)
E <- envelope(fit, nsim=3, correction="border", global=TRUE, nrep=100)
}
# Envelope of K function for simulations from cluster model
fit <- kppm(redwood ~1, "Thomas")
\dontrun{
plot(envelope(fit, Gest))
plot(envelope(fit, Gest, global=TRUE))
}
\testonly{
E <- envelope(fit, Gest, correction="rs", nsim=3, global=TRUE, nrep=100)
}
# Envelope of G function under CSR
\dontrun{
plot(envelope(X, Gest))
}
\testonly{
E <- envelope(X, Gest, correction="rs", nsim=3)
}
# Envelope of L function under CSR
# L(r) = sqrt(K(r)/pi)
\dontrun{
E <- envelope(X, Kest)
plot(E, sqrt(./pi) ~ r)
}
\testonly{
E <- envelope(X, Kest, correction="border", nsim=3)
plot(E, sqrt(./pi) ~ r)
}
# Simultaneous critical envelope for L function
# (alternatively, use Lest)
\dontrun{
plot(envelope(X, Kest, transform=expression(sqrt(./pi)), global=TRUE))
}
\testonly{
E <- envelope(X, Kest, nsim=3, correction="border",
transform=expression(sqrt(./pi)), global=TRUE)
}
## One-sided envelope
\dontrun{
plot(envelope(X, Lest, alternative="less"))
}
\testonly{
E <- envelope(X, Lest, nsim=3, alternative="less")
}
# How to pass arguments needed to compute the summary functions:
# We want envelopes for Jcross(X, "A", "B")
# where "A" and "B" are types of points in the dataset 'demopat'
\dontrun{
plot(envelope(demopat, Jcross, i="A", j="B"))
}
\testonly{
plot(envelope(demopat, Jcross, correction="rs", i="A", j="B", nsim=3))
}
# Use of `simulate' expression
\dontrun{
plot(envelope(cells, Gest, simulate=expression(runifpoint(42))))
plot(envelope(cells, Gest, simulate=expression(rMaternI(100,0.02))))
}
\testonly{
plot(envelope(cells, Gest, correction="rs", simulate=expression(runifpoint(42)), nsim=3))
plot(envelope(cells, Gest, correction="rs", simulate=expression(rMaternI(100, 0.02)),
nsim=3, global=TRUE))
}
# Use of `simulate' function
\dontrun{
plot(envelope(amacrine, Kcross, simulate=rlabel))
}
\testonly{
plot(envelope(amacrine, Kcross, simulate=rlabel, nsim=3))
}
# Envelope under random toroidal shifts
\dontrun{
plot(envelope(amacrine, Kcross, i="on", j="off",
simulate=expression(rshift(amacrine, radius=0.25))))
}
# Envelope under random shifts with erosion
\dontrun{
plot(envelope(amacrine, Kcross, i="on", j="off",
simulate=expression(rshift(amacrine, radius=0.1, edge="erode"))))
}
# Envelope of INHOMOGENEOUS K-function with fitted trend
# The following is valid.
# Setting lambda=fit means that the fitted model is re-fitted to
# each simulated pattern to obtain the intensity estimates for Kinhom.
# (lambda=NULL would also be valid)
fit <- kppm(redwood ~1, clusters="MatClust")
\dontrun{
plot(envelope(fit, Kinhom, lambda=fit, nsim=19))
}
\testonly{
envelope(fit, Kinhom, lambda=fit, nsim=3)
}
# Note that the principle of symmetry, essential to the validity of
# simulation envelopes, requires that both the observed and
# simulated patterns be subjected to the same method of intensity
# estimation. In the following example it would be incorrect to set the
# argument 'lambda=red.dens' in the envelope command, because this
# would mean that the inhomogeneous K functions of the simulated
# patterns would be computed using the intensity function estimated
# from the original redwood data, violating the symmetry. There is
# still a concern about the fact that the simulations are generated
# from a model that was fitted to the data; this is only a problem in
# small datasets.
\dontrun{
red.dens <- density(redwood, sigma=bw.diggle)
plot(envelope(redwood, Kinhom, sigma=bw.diggle,
simulate=expression(rpoispp(red.dens))))
}
# Precomputed list of point patterns
\dontrun{
nX <- npoints(X)
PatList <- list()
for(i in 1:19) PatList[[i]] <- runifpoint(nX)
E <- envelope(X, Kest, nsim=19, simulate=PatList)
}
\testonly{
PatList <- list()
for(i in 1:3) PatList[[i]] <- runifpoint(10)
E <- envelope(X, Kest, nsim=3, simulate=PatList)
}
# re-using the same point patterns
\dontrun{
EK <- envelope(X, Kest, savepatterns=TRUE)
EG <- envelope(X, Gest, simulate=EK)
}
\testonly{
EK <- envelope(X, Kest, nsim=3, savepatterns=TRUE)
EG <- envelope(X, Gest, nsim=3, simulate=EK)
}
}
\author{
\spatstatAuthors.
}
\keyword{spatial}
\keyword{htest}
\keyword{hplot}
\keyword{iteration}