update vignette source for the spatial logistic map method

vignettes/si1_slm.Rmd

@@ -2,8 +2,8 @@
 title: "Spatial Logistic Map"
 author: "Wenbo Lyu"
 date: |
-  | Last update: 2026-02-15
-  | Last run: 2026-04-05
+  | Last update: 2026-07-20
+  | Last run: 2026-07-20
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteIndexEntry{SI1. Spatial Logistic Map}
@@ -86,13 +86,13 @@ We first construct three simulated species density distributions by combining a
 sim_trispecies = \(nx,ny,seed = 123){
   grid = expand.grid(seq(0, 10, length.out = nx),
                      seq(0, 10, length.out = ny))
-  cov.fun = \(d, range = 1.5, sill=1) sill * exp(-d/range)
+  cov.fun = \(d, range=1.5, sill=1) sill * exp(-d/range)
   dist.mat = fields::rdist(grid)
   cov.mat = cov.fun(dist.mat, range=1.5, sill=1)
   set.seed(seed)
   res = replicate(3, {
     MASS::mvrnorm(1, rep(0, nrow(grid)), cov.mat) |>
-      pmax(0) |>
+      #pmax(0) |>
       sdsfun::normalize_vector(0,1) |>
       matrix(nrow = nx, ncol = ny) |>
       terra::rast()
@@ -103,14 +103,14 @@ sim_trispecies = \(nx,ny,seed = 123){
 species = sim_trispecies(20,20)
 names(species) = letters[1:3]
 species
-## class       : SpatRaster 
+## class       : SpatRaster
 ## size        : 20, 20, 3  (nrow, ncol, nlyr)
 ## resolution  : 1, 1  (x, y)
 ## extent      : 0, 20, 0, 20  (xmin, xmax, ymin, ymax)
-## coord. ref. :  
+## coord. ref. : 
 ## source(s)   : memory
-## names       : a, b, c 
-## min values  : 0, 0, 0 
+## names       : a, b, c
+## min values  : 0, 0, 0
 ## max values  : 1, 1, 1
 
 options(terra.pal = grDevices::terrain.colors(100,rev = T))
@@ -125,28 +125,28 @@ terra::plot(species, nc = 3,
 
 <br>
 
-We assume an underlying causal interaction structure among species, where species *a* influences *b*, and *b* in turn influences *c* (i.e., *a* → *b* → *c*). The intrinsic growth rates of species a, b, and c are uniformly assigned a value of 0.2. Species a exerts an effect of 1 on species b, and species b exerts an effect of 1 on species c. All other interspecific influence parameters are set to 0. This setup provides a controlled environment to test spatial causality detection methods under known dynamic interactions.
+We assume an underlying causal interaction structure among species, where species *a* influences *b*, and *b* in turn influences *c* (i.e., *a* → *b* → *c*). The intrinsic growth rates of species a, b, and c are assigned to 0.75, 0.78 and 0.76 respectively. Species a exerts an effect of 0.04 on species b, and species b exerts an effect of 0.04 on species c. All other interspecific influence parameters are set to 0. This setup provides a controlled environment to test spatial causality detection methods under known dynamic interactions.
 
 
 ``` r
 simv = spEDM::slm(species, x = "a", y = "b", z = "c", k = 4, step = 15, transient = 1, interact = "local",
-                  alpha_x = 0.2, alpha_y = 0.2, alpha_z = 0.2,
-                  beta_xy = 1, beta_xz = 0, beta_yx = 0, beta_yz = 1, beta_zx = 0, beta_zy = 0)
+                  alpha_x = 0.75, alpha_y = 0.78, alpha_z = 0.76,
+                  beta_xy = 0.04, beta_xz = 0, beta_yx = 0, beta_yz = 0.04, beta_zx = 0, beta_zy = 0)
 
 species_evolution = species
 terra::values(species_evolution[["a"]]) = simv$x
 terra::values(species_evolution[["b"]]) = simv$y
 terra::values(species_evolution[["c"]]) = simv$z
 species_evolution
-## class       : SpatRaster 
+## class       : SpatRaster
 ## size        : 20, 20, 3  (nrow, ncol, nlyr)
 ## resolution  : 1, 1  (x, y)
 ## extent      : 0, 20, 0, 20  (xmin, xmax, ymin, ymax)
-## coord. ref. :  
+## coord. ref. : 
 ## source(s)   : memory
-## names       :         a,         b,         c 
-## min values  : 0.8553408, 0.9733337, 0.9886246 
-## max values  : 0.8611597, 0.9803933, 1.0008822
+## names       :        a,        b,        c
+## min values  : 0.652826,  0.65015,   0.6594
+## max values  : 0.672098, 0.673739, 0.681139
 
 terra::plot(species_evolution, nc = 3,
             mar = rep(0.1,4),
@@ -155,29 +155,29 @@ terra::plot(species_evolution, nc = 3,
             legend = FALSE)
 ```
 
-![**Figure 2**. Species distributions following spatiotemporal interaction and evolution after 20 simulation steps with 4-neighbor interactions.](../man/figures/slm/slm1-1.png)
+![**Figure 2**. Species distributions following spatiotemporal interaction and evolution after 15 simulation steps with 4-neighbor interactions.](../man/figures/slm/slm1-1.png)
 
 <br>
 
 
 ``` r
 simv = spEDM::slm(species, x = "a", y = "b", z = "c", k = 4, step = 15, transient = 1, interact = "neighbors",
-                  alpha_x = 0.2, alpha_y = 0.2, alpha_z = 0.2, beta_xy = 1, beta_xz = 0, beta_yx = 0, beta_yz = 1, beta_zx = 0, beta_zy = 0)
+                  alpha_x = 0.75, alpha_y = 0.78, alpha_z = 0.76, beta_xy = 0.04, beta_xz = 0, beta_yx = 0, beta_yz = 0.04, beta_zx = 0, beta_zy = 0)
 
 species_evolution = species
 terra::values(species_evolution[["a"]]) = simv$x
 terra::values(species_evolution[["b"]]) = simv$y
 terra::values(species_evolution[["c"]]) = simv$z
 species_evolution
-## class       : SpatRaster 
+## class       : SpatRaster
 ## size        : 20, 20, 3  (nrow, ncol, nlyr)
 ## resolution  : 1, 1  (x, y)
 ## extent      : 0, 20, 0, 20  (xmin, xmax, ymin, ymax)
-## coord. ref. :  
+## coord. ref. : 
 ## source(s)   : memory
-## names       :         a,         b,         c 
-## min values  : 0.8553408, 0.9718102, 0.9887262 
-## max values  : 0.8611597, 0.9803232, 0.9986816
+## names       :        a,        b,        c
+## min values  : 0.652826, 0.649896, 0.659449
+## max values  : 0.672098, 0.673748, 0.681138
 
 terra::plot(species_evolution, nc = 3,
             mar = rep(0.1,4),
@@ -186,4 +186,4 @@ terra::plot(species_evolution, nc = 3,
             legend = FALSE)
 ```
 
-![**Figure 3**. Species distributions after 20 simulation steps with 4-neighbor interactions and neighbor-averaged cross-variable dynamics.](../man/figures/slm/slm2-1.png)
+![**Figure 3**. Species distributions after 15 simulation steps with 4-neighbor interactions and neighbor-averaged cross-variable dynamics.](../man/figures/slm/slm2-1.png)

vignettes/si1_slm.Rmd.orig

@@ -2,7 +2,7 @@
 title: "Spatial Logistic Map"
 author: "Wenbo Lyu"
 date: |
-  | Last update: 2026-02-15
+  | Last update: 2026-07-20
   | Last run: `r Sys.Date()`
 output: rmarkdown::html_vignette
 vignette: >
@@ -91,13 +91,13 @@ We first construct three simulated species density distributions by combining a
 sim_trispecies = \(nx,ny,seed = 123){
   grid = expand.grid(seq(0, 10, length.out = nx),
                      seq(0, 10, length.out = ny))
-  cov.fun = \(d, range = 1.5, sill=1) sill * exp(-d/range)
+  cov.fun = \(d, range=1.5, sill=1) sill * exp(-d/range)
   dist.mat = fields::rdist(grid)
   cov.mat = cov.fun(dist.mat, range=1.5, sill=1)
   set.seed(seed)
   res = replicate(3, {
     MASS::mvrnorm(1, rep(0, nrow(grid)), cov.mat) |>
-      pmax(0) |>
+      #pmax(0) |>
       sdsfun::normalize_vector(0,1) |>
       matrix(nrow = nx, ncol = ny) |>
       terra::rast()
@@ -119,12 +119,12 @@ terra::plot(species, nc = 3,
 
 <br>
 
-We assume an underlying causal interaction structure among species, where species *a* influences *b*, and *b* in turn influences *c* (i.e., *a* → *b* → *c*). The intrinsic growth rates of species a, b, and c are uniformly assigned a value of 0.2. Species a exerts an effect of 1 on species b, and species b exerts an effect of 1 on species c. All other interspecific influence parameters are set to 0. This setup provides a controlled environment to test spatial causality detection methods under known dynamic interactions.
+We assume an underlying causal interaction structure among species, where species *a* influences *b*, and *b* in turn influences *c* (i.e., *a* → *b* → *c*). The intrinsic growth rates of species a, b, and c are assigned to 0.75, 0.78 and 0.76 respectively. Species a exerts an effect of 0.04 on species b, and species b exerts an effect of 0.04 on species c. All other interspecific influence parameters are set to 0. This setup provides a controlled environment to test spatial causality detection methods under known dynamic interactions.
 
-```{r slm1,fig.width=6.55,fig.height=2.60,fig.dpi=100,fig.cap=knitr::asis_output("**Figure 2**. Species distributions following spatiotemporal interaction and evolution after 20 simulation steps with 4-neighbor interactions.")}
+```{r slm1,fig.width=6.55,fig.height=2.60,fig.dpi=100,fig.cap=knitr::asis_output("**Figure 2**. Species distributions following spatiotemporal interaction and evolution after 15 simulation steps with 4-neighbor interactions.")}
 simv = spEDM::slm(species, x = "a", y = "b", z = "c", k = 4, step = 15, transient = 1, interact = "local",
-                  alpha_x = 0.2, alpha_y = 0.2, alpha_z = 0.2,
-                  beta_xy = 1, beta_xz = 0, beta_yx = 0, beta_yz = 1, beta_zx = 0, beta_zy = 0)
+                  alpha_x = 0.75, alpha_y = 0.78, alpha_z = 0.76,
+                  beta_xy = 0.04, beta_xz = 0, beta_yx = 0, beta_yz = 0.04, beta_zx = 0, beta_zy = 0)
 
 species_evolution = species
 terra::values(species_evolution[["a"]]) = simv$x
@@ -141,9 +141,9 @@ terra::plot(species_evolution, nc = 3,
 
 <br>
 
-```{r slm2,fig.width=6.55,fig.height=2.60,fig.dpi=100,fig.cap=knitr::asis_output("**Figure 3**. Species distributions after 20 simulation steps with 4-neighbor interactions and neighbor-averaged cross-variable dynamics.")}
+```{r slm2,fig.width=6.55,fig.height=2.60,fig.dpi=100,fig.cap=knitr::asis_output("**Figure 3**. Species distributions after 15 simulation steps with 4-neighbor interactions and neighbor-averaged cross-variable dynamics.")}
 simv = spEDM::slm(species, x = "a", y = "b", z = "c", k = 4, step = 15, transient = 1, interact = "neighbors",
-                  alpha_x = 0.2, alpha_y = 0.2, alpha_z = 0.2, beta_xy = 1, beta_xz = 0, beta_yx = 0, beta_yz = 1, beta_zx = 0, beta_zy = 0)
+                  alpha_x = 0.75, alpha_y = 0.78, alpha_z = 0.76, beta_xy = 0.04, beta_xz = 0, beta_yx = 0, beta_yz = 0.04, beta_zx = 0, beta_zy = 0)
 
 species_evolution = species
 terra::values(species_evolution[["a"]]) = simv$x