---
title: "Spatially Convergent Partial Cross Mapping"
author: "Wenbo Lv"
date: |
  | Last update: 2026-02-01
  | Last run: 2026-02-02
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{6. Spatially Convergent Partial Cross Mapping}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---



## Model principles

The methodological details are pending peer review and will be made available thereafter.

## Usage examples

### Example of spatial vector data

Load the `spEDM` package and columbus OH spatial analysis dataset:


``` r
if (!requireNamespace("spEDM")) install.packages("spEDM")
## Loading required namespace: spEDM

columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))
columbus
## Simple feature collection with 49 features and 6 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 5.874907 ymin: 10.78863 xmax: 11.28742 ymax: 14.74245
## Projected CRS: Undefined Cartesian SRS with unknown unit
## # A tibble: 49 × 7
##    hoval   inc  crime  open plumb discbd                                     geom
##    <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl>                                <POLYGON>
##  1  80.5 19.5  15.7   2.85  0.217   5.03 ((8.624129 14.23698, 8.5597 14.74245, 8…
##  2  44.6 21.2  18.8   5.30  0.321   4.27 ((8.25279 14.23694, 8.282758 14.22994, …
##  3  26.4 16.0  30.6   4.53  0.374   3.89 ((8.653305 14.00809, 8.81814 14.00205, …
##  4  33.2  4.48 32.4   0.394 1.19    3.7  ((8.459499 13.82035, 8.473408 13.83227,…
##  5  23.2 11.3  50.7   0.406 0.625   2.83 ((8.685274 13.63952, 8.677577 13.72221,…
##  6  28.8 16.0  26.1   0.563 0.254   3.78 ((9.401384 13.5504, 9.434411 13.69427, …
##  7  75    8.44  0.178 0     2.40    2.74 ((8.037741 13.60752, 8.062716 13.60452,…
##  8  37.1 11.3  38.4   3.48  2.74    2.89 ((8.247527 13.58651, 8.2795 13.5965, 8.…
##  9  52.6 17.6  30.5   0.527 0.891   3.17 ((9.333297 13.27242, 9.671007 13.27361,…
## 10  96.4 13.6  34.0   1.55  0.558   4.33 ((10.08251 13.03377, 10.0925 13.05275, …
## # ℹ 39 more rows
```

We demonstrate how spatial vector data can be used in SCPCM analysis through a causal example examining the influences of the level of burglary incidents in a neighbourhood on house values, with neighbourhood household income included as a conditioning variable.

Determine minimum embedding dimensions:


``` r
spEDM::fnn(columbus,"crime",E = 1:10)
##        E:1        E:2        E:3        E:4        E:5        E:6        E:7 
## 0.79591837 0.53061224 0.63265306 0.51020408 0.12244898 0.04081633 0.00000000 
##        E:8 
## 0.00000000
spEDM::fnn(columbus,"hoval",E = 1:10)
##        E:1        E:2        E:3        E:4        E:5        E:6        E:7 
## 0.85714286 0.77551020 0.51020408 0.61224490 0.22448980 0.08163265 0.00000000 
##        E:8 
## 0.00000000
spEDM::fnn(columbus,"inc",E = 1:10)
##        E:1        E:2        E:3        E:4        E:5        E:6        E:7 
## 0.73469388 0.24489796 0.30612245 0.38775510 0.24489796 0.04081633 0.00000000 
##        E:8 
## 0.00000000
```

Self prediction for parameter turning:


``` r
spEDM::simplex(columbus,"crime","crime",E = 7:10,k=12)
## The suggested E,k,tau for variable crime is 8, 12 and 1
spEDM::simplex(columbus,"hoval","hoval",E = 7:10,k=12)
## The suggested E,k,tau for variable hoval is 7, 12 and 1
spEDM::simplex(columbus,"inc","inc",E = 7:10,k=12)
## The suggested E,k,tau for variable inc is 8, 12 and 1
```

Conduct SCPCM:


``` r
crime_hoval = spEDM::scpcm(data = columbus,
                           cause = "crime",
                           effect = "hoval",
                           conds = "inc",
                           libsizes = seq(5, 45, by = 5),
                           E = c(8,7,8),
                           k = 12,
                           progressbar = FALSE)
crime_hoval
## -------------------------------------- 
## ***partial cross mapping prediction*** 
## -------------------------------------- 
##   libsizes crime->hoval hoval->crime
## 1        5   0.08948688  0.022401607
## 2       10   0.14511673  0.062986079
## 3       15   0.16849249  0.080653365
## 4       20   0.20940427  0.040974217
## 5       25   0.23160147  0.026613757
## 6       30   0.24709326  0.001942294
## 7       35   0.29707812 -0.014879976
## 8       40   0.33591810 -0.028758126
## 9       45   0.36337200 -0.040204136
## 
## ------------------------------ 
## ***cross mapping prediction*** 
## ------------------------------ 
##   libsizes crime->hoval hoval->crime
## 1        5    0.2224861    0.1378071
## 2       10    0.3023880    0.1880328
## 3       15    0.4005255    0.2085278
## 4       20    0.4681692    0.2203727
## 5       25    0.5098130    0.2293076
## 6       30    0.5467173    0.2326005
## 7       35    0.5732757    0.2331852
## 8       40    0.5822545    0.2361204
## 9       45    0.5883504    0.2364234
```

Visualize the result:


``` r
if (!requireNamespace("cowplot")) install.packages("cowplot")
## Loading required namespace: cowplot

fig1a = plot(crime_hoval,partial = FALSE,ylimits = c(0.1,0.75))
fig1b = plot(crime_hoval,partial = TRUE,ylimits = c(-0.05,0.55))
fig1 = cowplot::plot_grid(fig1a,fig1b,ncol = 2,label_fontfamily = 'serif',
                          labels = paste0('(',letters[1:2],')'))
fig1
```

![**Figure 1**. The cross mapping between crime and house value. **a** Crime–hoval causality without accounting for covariates. **b** Crime–hoval causality controlling for household income.](../man/figures/scpcm/fig1-1.png)

<br>

### Example of spatial raster data

Load the `spEDM` package and simulate raster data with a cyclic interaction structure $x \rightarrow y \rightarrow z \rightarrow x$:


``` r
if (!requireNamespace("fields")) install.packages("fields")
## Loading required namespace: fields
if (!requireNamespace("MASS")) install.packages("MASS")

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)
  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) |>
      sdsfun::normalize_vector(0,1) |>
      matrix(nrow = nx, ncol = ny) |>
      terra::rast()
  }, simplify = FALSE)
  terra::rast(res)
}

species = sim_trispecies(20,20, seed = 42)
names(species) = c("x","y","z")

sim = spEDM::slm(species, x = "x", y = "y", z = "z", k = 4,
                 step = 15, transient = 1, threshold = Inf,
                 aggregate_fn = \(.x) mean(.x, na.rm = TRUE),
                 alpha_x = 0.05, alpha_y = 0.05, alpha_z = 0.05,
                 beta_xy = 1, beta_xz = 0, beta_yx = 0, beta_yz = 1,
                 beta_zx = 1, beta_zy = 0)

terra::values(species[["x"]]) = sim$x
terra::values(species[["y"]]) = sim$y
terra::values(species[["z"]]) = sim$z
species
## 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. :  
## source(s)   : memory
## names       :         x,         y,        z 
## min values  : 0.9984091, 0.9983558, 0.998285 
## max values  : 1.0009110, 1.0011445, 1.001050
```

Determine minimum embedding dimensions:


``` r
spEDM::fnn(species, "x")
##       E:1       E:2       E:3       E:4       E:5       E:6       E:7       E:8 
## 0.8937824 0.3125000 0.0100000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 
##       E:9 
## 0.0000000
spEDM::fnn(species, "y")
##       E:1       E:2       E:3       E:4       E:5       E:6       E:7       E:8 
## 0.9333333 0.2525000 0.0425000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 
##       E:9 
## 0.0000000
spEDM::fnn(species, "z")
##       E:1       E:2       E:3       E:4       E:5       E:6       E:7       E:8 
## 0.9238845 0.3375000 0.0625000 0.0200000 0.0000000 0.0000000 0.0000000 0.0000000 
##       E:9 
## 0.0000000
```

Self prediction for parameter turning:


``` r
s1 = spEDM::simplex(species, "x", "x", E = 5:10, k = 15, tau = 1)
s2 = spEDM::simplex(species, "y", "y", E = 5:10, k = 15, tau = 1)
s3 = spEDM::simplex(species, "z", "z", E = 5:10, k = 15, tau = 1)
list(s1,s2,s3)
## [[1]]
## The suggested E,k,tau for variable x is 7, 15 and 1 
## 
## [[2]]
## The suggested E,k,tau for variable y is 9, 15 and 1 
## 
## [[3]]
## The suggested E,k,tau for variable z is 5, 15 and 1
```

Due to the statistical aggregation performed after simulating with the spatial logistic map, we adopt a uniform embedding dimension for x, y, and z to reduce inference bias:


``` r
if (!requireNamespace("purrr")) install.packages("purrr")
## Loading required namespace: purrr
simplex_res = purrr::map2_dfr(list(s1,s2,s3), c("x","y","z"),
                              \(.list,.name) dplyr::mutate(.list$xmap,variable = .name))
ggplot2::ggplot(data = simplex_res) +
  ggplot2::geom_line(ggplot2::aes(x = E, y = rho, color = variable)) +
  ggplot2::theme_classic()
```

![**Figure 2**. Self cross prediction skills for three variables.](../man/figures/scpcm/fig2-1.png)

<br>

Since the self-prediction performance of the y variable is relatively weaker than that of x and z, we select the embedding dimension that yields the strongest predictive performance for y as the final dimension, that is $E = 9$.

Investigate the causation between x and z, with y as control variables:


``` r
xz = spEDM::scpcm(species, "x", "z", "y", E = 9, k = 15,
                  libsizes = matrix(seq(50,400,50), ncol = 1),
                  progressbar = FALSE)
xz
## -------------------------------------- 
## ***partial cross mapping prediction*** 
## -------------------------------------- 
##   libsizes       x->z      z->x
## 1       50 0.07977338 0.1020714
## 2      100 0.10473775 0.1762694
## 3      150 0.14175015 0.1959109
## 4      200 0.20562278 0.2543825
## 5      250 0.25500417 0.3300376
## 6      300 0.27904324 0.3956866
## 7      350 0.31249554 0.4597477
## 8      400 0.28091955 0.4843444
## 
## ------------------------------ 
## ***cross mapping prediction*** 
## ------------------------------ 
##   libsizes      x->z      z->x
## 1       50 0.1542880 0.1368370
## 2      100 0.1827606 0.2217753
## 3      150 0.2426258 0.2781118
## 4      200 0.3239733 0.3487456
## 5      250 0.4035323 0.4185710
## 6      300 0.4653763 0.4835619
## 7      350 0.5190529 0.5553776
## 8      400 0.5658039 0.5938894
```

Visualize the result:


``` r
fig2a = plot(xz,partial = FALSE,ylimits = c(0.05,0.65))
fig2b = plot(xz,partial = TRUE,ylimits = c(0.05,0.65))
fig2 = cowplot::plot_grid(fig2a,fig2b,ncol = 2,label_fontfamily = 'serif',
                          labels = paste0('(',letters[1:2],')'))
fig2
```

![**Figure 3**. The cross mapping between x and z. **a** x–z causality without accounting for y. **b** x–z causality controlling for y.](../man/figures/scpcm/fig3-1.png)
