# Plot function. Plots objects of class `testmc`. Supports global and local types, multiple marks, and fully customizable appearance via `layers` or `...`. ```r ## S3 method for class 'testmc' plot(x, ...) ``` ## Arguments - `x`: An object of class `testmc` or `global_envelope`. - `...`: Ignored. ## Returns Invisibly returns the ggplot object. ## Additional graphical customization The object returned by `plot.testmc()` is a `ggplot` object. This means that all standard graphical components from the `ggplot2` package (such as layers, scales, and themes) can be added to the plot using the `+` operator. This allows users to further customize labels, colors, axes, and the overall appearance of the plot. ## Examples ```r library(spatstat.geom) library(spatstat.random) library(spatstat.explore) library(spatstat.linnet) library(ggplot2) ####################################### ## 1. Planar point patterns (ppp) ####################################### # Global mark correlation (real-valued marks) X_ppp <- rpoispp(200) marks(X_ppp) <- data.frame(m1 = runif(npoints(X_ppp), 1, 10)) tc_ppp1 <- testmc(X_ppp, fun = mcorr.ppp, fun_args = list(ftype = "stoyan", method = "density")) plot(tc_ppp1) + labs( title = "Global mark correlation (ppp)", x = expression(r), y = "Observed / Envelope" ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), axis.title = element_text(size = 12), axis.text = element_text(size = 10), legend.title = element_text(size = 11), legend.text = element_text(size = 10) ) # Local mark correlation (real-valued marks) X_ppp2 <- rpoispp(200) marks(X_ppp2) <- data.frame(m1 = runif(npoints(X_ppp2), 1, 10)) tc_ppp2 <- testmc(X_ppp2, fun = lmcorr.ppp, fun_args = list(ftype = "stoyan", method = "density")) plot(tc_ppp2[[1]]) + labs( title = "Local mark correlation (ppp)", x = expression(r), y = "Local correlation" ) + theme_classic() + theme( plot.title = element_text(size = 13, face = "bold"), axis.title = element_text(size = 12), axis.text = element_text(size = 10), legend.title = element_text(size = 11), legend.text = element_text(size = 10), legend.position = "right" ) plot(tc_ppp2) + labs( title = "Effective range", x = expression(r), y = "Id of significant points" ) + theme_classic() + theme( plot.title = element_text(size = 23, face = "bold"), axis.title = element_text(size = 12), axis.text = element_text(size = 20), legend.title = element_text(size = 21), legend.text = element_text(size = 30), legend.position = "right" ) ####################################### ## 2. Linear network point patterns (lpp) ####################################### X_lpp <- rpoislpp(40, simplenet) marks(X_lpp) <- data.frame(m1 = runif(npoints(X_lpp), 1, 10)) tc_lpp1 <- testmc(X_lpp, fun = mcorr.lpp, fun_args = list(ftype = "stoyan", method = "density")) plot(tc_lpp1) + labs( title = "Global mark correlation (lpp)", x = expression(r), y = "Observed / Envelope" ) + theme_bw() + theme( plot.title = element_text(size = 14, face = "bold"), axis.title = element_text(size = 12), axis.text = element_text(size = 10), legend.title = element_text(size = 11), legend.text = element_text(size = 10), legend.position = "top" ) # Local functional mark correlation (function-valued marks) marks(X_lpp) <- data.frame( t1 = runif(npoints(X_lpp), 1, 10), t2 = runif(npoints(X_lpp), 1, 10) ) tc_lpp2 <- testmc(X_lpp, fun = lfmcorr, fun_args = list(ftype = "stoyan", method = "density")) plot(tc_lpp2[[1]]) + labs( title = "Local functional mark correlation (lpp)", x = expression(r), y = "Local correlation" ) + theme_light() + theme( plot.title = element_text(size = 13, face = "bold"), axis.title = element_text(size = 12), axis.text = element_text(size = 10), legend.title = element_text(size = 11), legend.text = element_text(size = 10), legend.position = "bottom" ) ``` ## Author(s) Mehdi Moradi m2.moradi@yahoo.com

plot.testmc function