# envelope.lpp

##### Envelope for Point Patterns on Linear Network

Enables envelopes to be computed for point patterns on a linear network.

- Keywords
- spatial

##### Usage

```
# S3 method for lpp
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)
```
# S3 method for lppm
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)

##### Arguments

- Y
A point pattern on a linear network (object of class

`"lpp"`

) or a fitted point process model on a linear network (object of class`"lppm"`

).- fun
Function that is to be computed for each simulated pattern.

- nsim
Number of simulations to perform.

- nrank
Integer. Rank of the envelope value amongst the

`nsim`

simulated values. A rank of 1 means that the minimum and maximum simulated values will be used.- …
Extra arguments passed to

`fun`

.- funargs
A list, containing extra arguments to be passed to

`fun`

.- funYargs
Optional. A list, containing extra arguments to be passed to

`fun`

when applied to the original data`Y`

only.- simulate
Optional. Specifies how to generate the simulated point patterns. If

`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 function, then this function will be repeatedly applied to the data pattern`Y`

to obtain`nsim`

simulated patterns. 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.- fix.n
Logical. If

`TRUE`

, simulated patterns will have the same number of points as the original data pattern.- fix.marks
Logical. If

`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.- verbose
Logical flag indicating whether to print progress reports during the simulations.

- transform
Optional. A transformation to be applied to the function values, before the envelopes are computed. An expression object (see Details).

- global
Logical flag indicating whether envelopes should be pointwise (

`global=FALSE`

) or simultaneous (`global=TRUE`

).- ginterval
Optional. A vector of length 2 specifying the interval of \(r\) values for the simultaneous critical envelopes. Only relevant if

`global=TRUE`

.- use.theory
Logical value indicating whether to use the theoretical value, computed by

`fun`

, as the reference value for simultaneous envelopes. Applicable only when`global=TRUE`

.- alternative
Character string determining whether the envelope corresponds to a two-sided test (

`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"`

).- 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 \(r\), when computing the global envelopes. Applicable only when

`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)`

.- clamp
Logical value indicating how to compute envelopes when

`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.- savefuns
Logical flag indicating whether to save all the simulated function values.

- savepatterns
Logical flag indicating whether to save all the simulated point patterns.

- 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

`global=TRUE`

and the simulations are not based on CSR.- VARIANCE
Logical. If

`TRUE`

, critical envelopes will be calculated as sample mean plus or minus`nSD`

times sample standard deviation.- nSD
Number of estimated standard deviations used to determine the critical envelopes, if

`VARIANCE=TRUE`

.- Yname
Character string that should be used as the name of the data point pattern

`Y`

when printing or plotting the results.- do.pwrong
Logical. If

`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.- envir.simul
Environment in which to evaluate the expression

`simulate`

, if not the current environment.

##### Details

This is a method for the generic
function `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.

if

`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`

.if

`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.If

`simulate`

is present, it specifies the type of simulation as explained below.If

`simulate`

is an expression (typically including a call to a random generator), then the expression will be repeatedly evaluated, and should yield random point patterns on the same linear network as`Y`

.If

`simulate`

is a function (typically including a call to a random generator), then the function will be repeatedly applied to the original point pattern`Y`

, and should yield random point patterns on the same linear network as`Y`

.If

`simulate`

is a list of point patterns, then these will be taken as the simulated point patterns. They should be 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.

##### Value

Function value table (object of class `"fv"`

)
with additional information,
as described in `envelope`

.

##### References

Ang, Q.W. (2010)
*Statistical methodology for events on a network*.
Master's thesis, School of Mathematics and Statistics, University of
Western Australia.

Ang, Q.W., Baddeley, A. and Nair, G. (2012)
Geometrically corrected second-order analysis of
events on a linear network, with applications to
ecology and criminology.
*Scandinavian Journal of Statistics* **39**, 591--617.

Okabe, A. and Yamada, I. (2001) The K-function method on a network and
its computational implementation. *Geographical Analysis*
**33**, 271-290.

##### See Also

##### Examples

```
# NOT RUN {
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)
# }
```

*Documentation reproduced from package spatstat, version 1.61-0, License: GPL (>= 2)*