Utility Functions

To simplify post-processing and visualization, two helper functions are provided below.

utils_process_surd_result() converts a surd_list object (as returned by an SURD computation routine) into a tidy tibble for plotting and further analysis.

utils_process_surd_result = \(surd_list,threshold = 0){
  I_unique = surd_list$unique
  I_synergistic = surd_list$synergistic
  I_redundant = surd_list$redundant
  info_leak = surd_list$info_leak

  df = tibble::tibble(
    label = c(
      paste0("U[", names(I_unique), "]"),
      paste0("S[", names(I_synergistic), "]"),
      paste0("R[", names(I_redundant), "]")
    ),
    value = c(as.numeric(I_unique),
              as.numeric(I_synergistic),
              as.numeric(I_redundant)),
    type = c(rep("unique", length(I_unique)),
             rep("synergistic", length(I_synergistic)),
             rep("redundant", length(I_redundant)))
  )

  df$value = df$value / sum(df$value)
  if(threshold > 0){
    df = dplyr::filter(df, value > threshold)
  }
  return(dplyr::bind_rows(df,tibble::tribble(~label,~value,~type,
                                             "Info Leak", info_leak, "info leak")))
}

utils_plot_surd() produces a SURD decomposition plot — a grouped bar chart of unique (U), synergistic (S), and redundant (R) components, along with a side panel showing the information leak.

utils_plot_surd = \(surd_list,threshold = 0,style = "shallow"){
  I_unique = surd_list$unique
  I_synergistic = surd_list$synergistic
  I_redundant = surd_list$redundant

  df = tibble::tibble(
    label = c(
      paste0("U[", names(I_unique), "]"),
      paste0("S[", names(I_synergistic), "]"),
      paste0("R[", names(I_redundant), "]")
    ),
    value = c(as.numeric(I_unique),
              as.numeric(I_synergistic),
              as.numeric(I_redundant)),
    type = c(rep("unique", length(I_unique)),
             rep("synergistic", length(I_synergistic)),
             rep("redundant", length(I_redundant)))
  )

  df$value = df$value / sum(df$value)
  if(threshold > 0){
    df = dplyr::filter(df, value > threshold)
  }
  df$label = factor(df$label, levels = df$label)

  if (style == "shallow") {
    colors = c(unique = "#ec9e9e", synergistic = "#fac58c", redundant = "#668392", infoleak = "#7f7f7f")
  } else {
    colors = c(unique = "#d62828", synergistic = "#f77f00", redundant = "#003049", infoleak = "gray")
  }

  p1 = ggplot2::ggplot(df, ggplot2::aes(x = label, y = value, fill = type)) +
    ggplot2::geom_col(color = "black", linewidth = 0.15, show.legend = FALSE) +
    ggplot2::scale_fill_manual(name = NULL, values = colors) +
    ggplot2::scale_x_discrete(name = NULL, labels = function(x) parse(text = x)) +
    ggplot2::scale_y_continuous(name = NULL, limits = c(0, 1), expand = c(0, 0),
                                breaks = c(0, 0.25, 0.5, 0.75, 1),
                                labels = c("0", "0.25", "0.5", "0.75", "1") ) +
    ggplot2::theme_bw() +
    ggplot2::theme(
      axis.ticks.x = ggplot2::element_blank(),
      axis.text.x = ggplot2::element_text(angle = 45, hjust = 1),
      panel.grid = ggplot2::element_blank(),
      panel.border = ggplot2::element_rect(color = "black", linewidth = 2)
    )

  df_leak = tibble::tibble(label = "Info Leak", value = surd_list$info_leak)
  p2 = ggplot2::ggplot(df_leak, ggplot2::aes(x = label, y = value)) +
    ggplot2::geom_col(fill = colors[4], color = "black", linewidth = 0.15) +
    ggplot2::scale_x_discrete(name = NULL, expand = c(0, 0)) +
    ggplot2::scale_y_continuous(name = NULL, limits = c(0, 1), expand = c(0, 0),
                                breaks = c(0,1), labels = c(0,1) ) +
    ggplot2::theme_bw() +
    ggplot2::theme(
      axis.ticks.x = ggplot2::element_blank(),
      panel.grid = ggplot2::element_blank(),
      panel.border = ggplot2::element_rect(color = "black", linewidth = 2)
    )

  patchwork::wrap_plots(p1, p2, ncol = 2, widths = c(10,1))
}

Example Cases

The following sections demonstrate SURD decomposition in different contexts, illustrating its flexibility across temporal and spatial causal analyses.

Air Pollution and Cardiovascular Health in Hong Kong

cvd = readr::read_csv(system.file("case/cvd.csv",package = "tEDM"))
head(cvd)
## # A tibble: 6 × 5
##     cvd   rsp   no2   so2    o3
##   <dbl> <dbl> <dbl> <dbl> <dbl>
## 1   214  73.7  74.5  19.1 17.4 
## 2   203  77.6  80.9  18.8 39.4 
## 3   202  64.8  67.1  13.8 56.4 
## 4   182  68.8  74.7  30.8  5.6 
## 5   181  49.4  62.3  23.1  3.6 
## 6   129  67.4  63.6  17.4  6.73

cvd_long = cvd |>
  tibble::rowid_to_column("id") |>
  tidyr::pivot_longer(cols = -id,
                      names_to = "variable", values_to = "value")

fig_cvds_ts = ggplot2::ggplot(cvd_long, ggplot2::aes(x = id, y = value, color = variable)) +
  ggplot2::geom_line(linewidth = 0.5) +
  ggplot2::labs(x = "Days (from 1995-3 to 1997-11)", y = "Concentrations or \nNO. of CVD admissions", color = "") +
  ggplot2::theme_bw() +
  ggplot2::theme(legend.direction = "horizontal",
                 legend.position = "inside",
                 legend.justification = c("center","top"),
                 legend.background = ggplot2::element_rect(fill = "transparent", color = NA))
fig_cvds_ts

Figure 1. Time series of air pollutants and confirmed CVD cases in Hong Kong from March 1995 to November 1997.

To investigate the causal influences of air pollutants on the incidence of cardiovascular diseases, we performed SURD analysis using a time lag step 3 and 10 discretization bins.

res_cvds = infocausality::surd(cvd, "cvd",
                               c("rsp", "no2", "so2", "o3"),
                               lag = 15, bin = 10, cores = 6)
## 
## SURD Decomposition Results:
##   Unique (U):
##     X1             : 0.0022
##     X2             : 0.0162
##     X3             : 0.0024
##     X4             : 0.0000
##   Synergistic (S):
##     X1-X2          : 0.0181
##     X1-X3          : 0.0198
##     X1-X4          : 0.0339
##     X2-X3          : 0.0293
##     X2-X4          : 0.0332
##     X3-X4          : 0.0477
##     X1-X2-X3       : 0.0343
##     X1-X2-X4       : 0.1084
##     X1-X3-X4       : 0.1540
##     X2-X3-X4       : 0.0426
##     X1-X2-X3-X4    : 0.4093
##   Redundant (R):
##     X1-X2          : 0.0034
##     X1-X3          : 0.0000
##     X1-X4          : 0.0007
##     X2-X3          : 0.0006
##     X2-X4          : 0.0034
##     X3-X4          : 0.0000
##     X1-X2-X3       : 0.0050
##     X1-X2-X4       : 0.0100
##     X1-X3-X4       : 0.0008
##     X2-X3-X4       : 0.0014
##     X1-X2-X3-X4    : 0.0230
##   Information Leak:  52.82%

The SURD results are shown in the figure below:

utils_plot_surd(res_cvds)

Figure 2. SURD decomposition results between different air pollutants and cardiovascular diseases.

Population Density and Its Drivers in Mainland China

popd_nb = spdep::read.gal(system.file("case/popd_nb.gal",package = "spEDM"))
## Warning in spdep::read.gal(system.file("case/popd_nb.gal", package = "spEDM")):
## neighbour object has 4 sub-graphs
popd = readr::read_csv(system.file("case/popd.csv",package = "spEDM"))
popd_sf = sf::st_as_sf(popd, coords = c("lon","lat"), crs = 4326)
popd_sf
## Simple feature collection with 2806 features and 5 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 74.9055 ymin: 18.2698 xmax: 134.269 ymax: 52.9346
## Geodetic CRS:  WGS 84
## # A tibble: 2,806 × 6
##     popd  elev   tem   pre slope          geometry
##  * <dbl> <dbl> <dbl> <dbl> <dbl>       <POINT [°]>
##  1  780.     8  17.4 1528. 0.452 (116.912 30.4879)
##  2  395.    48  17.2 1487. 0.842 (116.755 30.5877)
##  3  261.    49  16.0 1456. 3.56  (116.541 30.7548)
##  4  258.    23  17.4 1555. 0.932  (116.241 30.104)
##  5  211.   101  16.3 1494. 3.34   (116.173 30.495)
##  6  386.    10  16.6 1382. 1.65  (116.935 30.9839)
##  7  350.    23  17.5 1569. 0.346 (116.677 30.2412)
##  8  470.    22  17.1 1493. 1.88  (117.066 30.6514)
##  9 1226.    11  17.4 1526. 0.208 (117.171 30.5558)
## 10  137.   598  13.9 1458. 5.92  (116.208 30.8983)
## # ℹ 2,796 more rows

res_popd = infocausality::surd(popd_sf,"popd",c("elev","tem","pre","slope"),
                               lag = 3, bin = 10, nb = popd_nb, cores = 6)
## 
## SURD Decomposition Results:
##   Unique (U):
##     X1             : 0.0046
##     X2             : 0.0000
##     X3             : 0.0398
##     X4             : 0.0036
##   Synergistic (S):
##     X1-X2          : 0.0369
##     X1-X3          : 0.0471
##     X1-X4          : 0.0885
##     X2-X3          : 0.0468
##     X2-X4          : 0.0765
##     X3-X4          : 0.0877
##     X1-X2-X3       : 0.0384
##     X1-X2-X4       : 0.0597
##     X1-X3-X4       : 0.0463
##     X2-X3-X4       : 0.1577
##     X1-X2-X3-X4    : 0.1095
##   Redundant (R):
##     X1-X2          : 0.0000
##     X1-X3          : 0.0089
##     X1-X4          : 0.0000
##     X2-X3          : 0.0130
##     X2-X4          : 0.0027
##     X3-X4          : 0.0138
##     X1-X2-X3       : 0.0056
##     X1-X2-X4       : 0.0000
##     X1-X3-X4       : 0.0120
##     X2-X3-X4       : 0.0110
##     X1-X2-X3-X4    : 0.0897
##   Information Leak:  64.64%

utils_plot_surd(res_popd)

Figure 3. SURD decomposition results between population density and its Drivers.

Influence of Climatic and Topographic Factors on Net Primary Productivity (NPP) in Mainland China

npp = terra::rast(system.file("case/npp.tif", package = "spEDM"))
npp
## class       : SpatRaster 
## size        : 404, 483, 5  (nrow, ncol, nlyr)
## resolution  : 10000, 10000  (x, y)
## extent      : -2625763, 2204237, 1877078, 5917078  (xmin, xmax, ymin, ymax)
## coord. ref. : CGCS2000_Albers 
## source      : npp.tif 
## names       :      npp,        pre,      tem,      elev,         hfp 
## min values  :   164.00,   384.3409, -47.8194, -122.2004,  0.03390418 
## max values  : 16606.33, 23878.3555, 263.6938, 5350.4902, 44.90312195

res_npp = infocausality::surd(npp,"npp",c("pre","tem","elev","hfp"),
                              lag = 2, bin = 10, cores = 6)
## 
## SURD Decomposition Results:
##   Unique (U):
##     X1             : 0.2060
##     X2             : 0.0004
##     X3             : 0.0001
##     X4             : 0.0000
##   Synergistic (S):
##     X1-X2          : 0.0477
##     X1-X3          : 0.0734
##     X1-X4          : 0.0360
##     X2-X3          : 0.0268
##     X2-X4          : 0.0049
##     X3-X4          : 0.0010
##     X1-X2-X3       : 0.0363
##     X1-X2-X4       : 0.0171
##     X1-X3-X4       : 0.0380
##     X2-X3-X4       : 0.0201
##     X1-X2-X3-X4    : 0.0796
##   Redundant (R):
##     X1-X2          : 0.2658
##     X1-X3          : 0.0020
##     X1-X4          : 0.0000
##     X2-X3          : 0.0000
##     X2-X4          : 0.0000
##     X3-X4          : 0.0000
##     X1-X2-X3       : 0.0439
##     X1-X2-X4       : 0.0000
##     X1-X3-X4       : 0.0065
##     X2-X3-X4       : 0.0000
##     X1-X2-X3-X4    : 0.0941
##   Information Leak:  40.78%

utils_plot_surd(res_npp)

Figure 4. SURD decomposition results between climatic and topographic factors and npp.

🧩 See also:

?infocausality::surd() for function details.
spEDM package for spatial empirical dynamic modeling.
tEDM package for temporal empirical dynamic modeling.

Synergistic-Unique-Redundant Decomposition of Causality (SURD)

Last update: 2025-10-30
Last run: 2025-10-30

Introduction

Utility Functions

Example Cases

Air Pollution and Cardiovascular Health in Hong Kong

Population Density and Its Drivers in Mainland China

Influence of Climatic and Topographic Factors on Net Primary Productivity (NPP) in Mainland China

Synergistic-Unique-Redundant Decomposition of Causality (SURD)

Last update: 2025-10-30 Last run: 2025-10-30

Introduction

Utility Functions

Example Cases

Air Pollution and Cardiovascular Health in Hong Kong

Population Density and Its Drivers in Mainland China

Influence of Climatic and Topographic Factors on Net Primary Productivity (NPP) in Mainland China

Last update: 2025-10-30
Last run: 2025-10-30