markstat (version 0.1.5)

testmc: Global envelope tests for random labelling.

Description

Generic function to perform global envelope test for random labelling using mark correlation functions.

Usage

testmc(X,
fun,
nsim = 100,
fun_args = list(),     
get_args = list(),     
rlabel_args = list()
)

Value

Either an object of class global_envelope or a list of such objects if the point pattern has multiple marks or the employed mark correlation function is of type LIMA.

Arguments

X

An object of class ppp or lpp.

fun

Function that computes the desired mark correlation function.

nsim

Number of permutations.

fun_args

Arguments passed to the chosen mark correlation function.

get_args

Arguments passed to the global_envelope_test.

rlabel_args

Arguments passed to the rlabel.

Author

Mehdi Moradi

Details

Generic function to perform global envelope test for random labelling using mark correlation functions.

If the point pattern has several real-valued marks, then the random labelling test will be performed for each mark individually, and the output will be a list of global envelop tests one per each mark.

If the mark correlation function is any of the LIMA family, a random labelling test will be performed for each individual point.

References

Eckardt, M., & Moradi, M. (2024). Marked spatial point processes: current state and extensions to point processes on linear networks. Journal of Agricultural, Biological and Environmental Statistics, 29(2), 346-378.

Moradi, M., & Eckardt, M. (2025). Inhomogeneous mark correlation functions for general marked point processes.

Eckardt, M., & Moradi, M. (2025). Local indicators of mark association for marked spatial point processes.

See Also

global_envelope_test, mcorr.ppp, mcorr.lpp, mcorrinhom.ppp, mcorrinhom.lpp, fmcorr, lmcorr.ppp, lmcorr.lpp.

Examples

# \donttest{
 library(spatstat.geom)
 library(spatstat.random)
 library(spatstat.explore)
 library(spatstat.linnet)

#######################################
## 1. Planar point patterns (ppp)
#######################################

# ---------------------------------------------------------
# GLOBAL MARK CORRELATION (real-valued marks)
# ---------------------------------------------------------
X <- rpoispp(200)
marks(X) <- data.frame(m1 = runif(npoints(X), 1, 10))

ts1 <- testmc(X,
              fun = mcorr.ppp,
              fun_args = list(ftype = "stoyan", method = "density"))
plot(ts1)
# Global mark correlation (real-valued marks)

ts2 <- testmc(X,
              fun = mcorrinhom.ppp,
              fun_args = list(ftype = "stoyan",
                               method = "density",
                               method_lambda = "kernel"))
plot(ts2)
# Inhomogeneous global mark correlation (real-valued marks)


# ---------------------------------------------------------
# GLOBAL FUNCTIONAL MARK CORRELATION (function-valued marks)
# ---------------------------------------------------------
marks(X) <- data.frame(
  t1 = runif(npoints(X), 1, 10),
  t2 = runif(npoints(X), 1, 10),
  t3 = runif(npoints(X), 1, 10),
  t4 = runif(npoints(X), 1, 10),
  t5 = runif(npoints(X), 1, 10)
)

ts3 <- testmc(X,
              fun = fmcorr,
              fun_args = list(ftype = "stoyan", method = "density"))
plot(ts3)
# Global functional mark correlation (function-valued marks)

ts4 <- testmc(X,
              fun = fmcorrinhom,
              fun_args = list(ftype = "stoyan",
                               method = "density",
                               method_lambda = "kernel",
                               bw = bw.scott))
plot(ts4)
# Inhomogeneous functional mark correlation (function-valued marks)


# ---------------------------------------------------------
# LOCAL MARK CORRELATION (real-valued marks)
# ---------------------------------------------------------
X <- rpoispp(200)
marks(X) <- data.frame(m1 = runif(npoints(X), 1, 10))

ts5 <- testmc(X,
              fun = lmcorr.ppp,
              fun_args = list(ftype = "stoyan", method = "density"))
plot(ts5[[1]])
# Local mark correlation (real-valued marks)

ts6 <- testmc(X,
              fun = lmcorrinhom.ppp,
              fun_args = list(ftype = "stoyan",
                               method = "density",
                               method_lambda = "kernel"))
plot(ts6[[1]])
# Inhomogeneous local mark correlation (real-valued marks)


# ---------------------------------------------------------
# LOCAL FUNCTIONAL MARK CORRELATION (function-valued marks)
# ---------------------------------------------------------
marks(X) <- data.frame(
  t1 = runif(npoints(X), 1, 10),
  t2 = runif(npoints(X), 1, 10),
  t3 = runif(npoints(X), 1, 10),
  t4 = runif(npoints(X), 1, 10),
  t5 = runif(npoints(X), 1, 10)
)

ts7 <- testmc(X,
              fun = lfmcorr,
              fun_args = list(ftype = "stoyan", method = "density"))
plot(ts7[[1]])
# Local functional mark correlation (function-valued marks)

ts8 <- testmc(X,
              fun = lfmcorrinhom,
              fun_args = list(ftype = "stoyan",
                               method = "density",
                               method_lambda = "kernel",
                               bw = bw.scott))
plot(ts8[[1]])
# Inhomogeneous local functional mark correlation (function-valued marks)


#######################################
## 2. Linear network point patterns (lpp)
#######################################

Xl <- rpoislpp(40, simplenet)

# ---------------------------------------------------------
# GLOBAL MARK CORRELATION (real-valued marks)
# ---------------------------------------------------------
marks(Xl) <- data.frame(m1 = runif(npoints(Xl), 1, 10))

ts9 <- testmc(Xl,
              fun = mcorr.lpp,
              fun_args = list(ftype = "stoyan", method = "density"))
plot(ts9)
# Global mark correlation on linear networks (real-valued marks)

ts10 <- testmc(Xl,
               fun = mcorrinhom.lpp,
               fun_args = list(ftype = "stoyan",
                                method = "density",
                                method_lambda = "kernel"))
plot(ts10)
# Inhomogeneous global mark correlation on linear networks (real-valued marks)


# ---------------------------------------------------------
# GLOBAL FUNCTIONAL MARK CORRELATION (function-valued marks)
# ---------------------------------------------------------
marks(Xl) <- data.frame(
  t1 = runif(npoints(Xl), 1, 10),
  t2 = runif(npoints(Xl), 1, 10),
  t3 = runif(npoints(Xl), 1, 10),
  t4 = runif(npoints(Xl), 1, 10),
  t5 = runif(npoints(Xl), 1, 10)
)

ts11 <- testmc(Xl,
               fun = fmcorr,
               fun_args = list(ftype = "stoyan", method = "density"))
plot(ts11)
# Global functional mark correlation on linear networks (function-valued marks)

ts12 <- testmc(Xl,
               fun = fmcorrinhom,
               fun_args = list(ftype = "stoyan",
                                method = "density",
                                method_lambda = "kernel",
                                bw = bw.scott.iso))
plot(ts12)
# Inhomogeneous global functional mark correlation on linear networks
# (function-valued marks)


# ---------------------------------------------------------
# LOCAL MARK CORRELATION (real-valued marks)
# ---------------------------------------------------------
marks(Xl) <- data.frame(m1 = runif(npoints(Xl), 1, 10))

ts13 <- testmc(Xl,
               fun = lmcorr.lpp,
               fun_args = list(ftype = "stoyan", method = "density"))
plot(ts13[[1]])
# Local mark correlation on linear networks (real-valued marks)

ts14 <- testmc(Xl,
               fun = lmcorrinhom.lpp,
               fun_args = list(ftype = "stoyan",
                                method = "density",
                                method_lambda = "kernel"))
plot(ts14[[1]])
# Inhomogeneous local mark correlation on linear networks (real-valued marks)


# ---------------------------------------------------------
# LOCAL FUNCTIONAL MARK CORRELATION (function-valued marks)
# ---------------------------------------------------------
marks(Xl) <- data.frame(
  t1 = runif(npoints(Xl), 1, 10),
  t2 = runif(npoints(Xl), 1, 10),
  t3 = runif(npoints(Xl), 1, 10),
  t4 = runif(npoints(Xl), 1, 10),
  t5 = runif(npoints(Xl), 1, 10)
)

ts15 <- testmc(Xl,
               fun = lfmcorr,
               fun_args = list(ftype = "stoyan", method = "density"))
plot(ts15[[1]])
# Local functional mark correlation on linear networks (function-valued marks)

ts16 <- testmc(Xl,
               fun = lfmcorrinhom,
               fun_args = list(ftype = "stoyan",
                                method = "density",
                                method_lambda = "kernel",
                                bw = bw.scott.iso))
plot(ts16[[1]])
# Inhomogeneous local functional mark correlation on linear networks
# (function-valued marks)

# }