"envelope"(Y, fun=linearK, nsim=99, nrank=1, ..., funargs=list(), funYargs=funargs, simulate=NULL, fix.n=FALSE, fix.marks=FALSE, verbose=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, do.pwrong=FALSE, envir.simul=NULL)
"envelope"(Y, fun=linearK, nsim=99, nrank=1, ..., funargs=list(), funYargs=funargs, simulate=NULL, fix.n=FALSE, fix.marks=FALSE, verbose=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, do.pwrong=FALSE, envir.simul=NULL)"lpp")
or a fitted point process model on a linear network
(object of class "lppm").
nsim simulated
values. A rank of 1 means that the minimum and maximum
simulated values will be used.
fun.
fun.
fun when applied to the original data Y only.
simulate is an expression in the R language, then this
expression will be evaluated nsim times,
to obtain nsim point patterns which are taken as the
simulated patterns from which the envelopes are computed.
If 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 simulate may be an object produced by the
envelope command: see Details.
TRUE, simulated patterns will have the
same number of points as the original data pattern.
TRUE, simulated patterns will have the
same number of points 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
of each type as the original data.
global=FALSE) or simultaneous (global=TRUE).
global=TRUE.
fun, as the reference value for simultaneous
envelopes. Applicable only when global=TRUE.
side="two.sided", the default)
or a one-sided test with a lower critical boundary
(side="less") or a one-sided test
with an upper critical boundary (side="greater").
global=TRUE.
Summary function values for distance r
will be divided by scale(r) before the
maximum deviation is computed. The resulting global envelopes
will have width proportional to scale(r).
alternative="less" or 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 clamp=FALSE (the default), these values are not changed.
If clamp=TRUE, any negative values are replaced by zero.
global=TRUE
and the simulations are not based on CSR.
TRUE, critical envelopes will be calculated
as sample mean plus or minus nSD times sample standard
deviation.
VARIANCE=TRUE.
Y when printing or plotting the results.
TRUE, the algorithm will also estimate
the true significance level of the “wrong” test (the test that
declares the summary function for the data to be significant
if it lies outside the pointwise critical boundary at any
point). This estimate is printed when the result is printed.
simulate,
if not the current environment.
"fv")
with additional information,
as described in envelope.
envelope
applicable to point patterns on a linear network.
The argument Y can be either a point pattern on a linear
network, or a fitted point process model on a linear network.
The function fun will be evaluated for the data
and also for nsim simulated point
patterns on the same linear network.
The upper and lower
envelopes of these evaluated functions will be computed
as described in envelope.
The type of simulation is determined as follows.
Y is a point pattern (object of class "lpp")
and simulate is missing or NULL,
then random point patterns will be generated according to
a Poisson point process on the linear network on which Y
is defined, with intensity estimated from Y.
Y is a fitted point process model (object of class
"lppm") and simulate is missing or NULL,
then random point patterns will be generated by simulating
from the fitted model.
simulate is present, it should be an expression that
can be evaluated to yield random point patterns on the same
linear network as Y.
The function fun should accept as its first argument
a point pattern on a linear network (object of class "lpp")
and should have another argument called r or a ...
argument.
envelope,
linearK
if(interactive()) {
ns <- 39
np <- 40
} else { ns <- np <- 3 }
X <- runiflpp(np, simplenet)
# uniform Poisson: random numbers of points
envelope(X, nsim=ns)
# uniform Poisson: conditional on observed number of points
envelope(X, fix.n=TRUE, nsim=ns)
# nonuniform Poisson
fit <- lppm(X ~x)
envelope(fit, nsim=ns)
#multitype
marks(X) <- sample(letters[1:2], np, replace=TRUE)
envelope(X, nsim=ns)
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