# Global envelope tests for random labelling. Generic function to perform global envelope test for random labelling using mark correlation functions. ```r testmc(X, fun, nsim = 100, fun_args = list(), get_args = list(), rlabel_args = list() ) ``` ## 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`. ## Returns 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. ## 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. ## Examples ```r 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) ``` ## 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`. ## Author(s) Mehdi Moradi m2.moradi@yahoo.com

testmc function