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Plot Correlation Matrices of Conservation Solutions

Source: R/splnr_plotting.R
splnr_plot_corrMat.Rd

The splnr_plot_corrMat() function visualizes a correlation matrix of prioritizr conservation solutions, typically computed using Cohen's Kappa. This helps in understanding the agreement or disagreement between different spatial plans.

Usage

splnr_plot_corrMat(
  x,
  colourGradient = c("#BB4444", "#FFFFFF", "#4477AA"),
  legendTitle = "Correlation \ncoefficient",
  AxisLabels = NULL,
  plotTitle = ""
)

Arguments

x

A numeric correlation matrix of prioritizr solutions.

colourGradient

A character vector of three color values:

  • colourGradient[1]: Color for high positive correlation.

  • colourGradient[2]: Color for no correlation (midpoint).

  • colourGradient[3]: Color for high negative correlation.

Defaults to c("#BB4444", "#FFFFFF", "#4477AA").

legendTitle

A character string for the title of the legend. Defaults to "Correlation \ncoefficient".

AxisLabels

A character vector of labels for the x and y axes of the correlation matrix, representing the names of the correlated solutions. If NULL (default), the column names of x will be used. The length of this vector must match the number of rows/columns in x.

plotTitle

A character string for the title of the plot. Defaults to "".

Value

A ggplot object representing the correlation matrix plot.

Details

Conservation planning often involves comparing the outputs of various conservation problems. One effective method for this is correlating solutions using metrics like Cohen's Kappa. This function takes a correlation matrix (e.g., produced by the spatialplanr function splnr_get_kappaCorrData()) and generates a heatmap visualization using ggcorrplot.

The plot highlights positive, negative, and no correlation using a color gradient, and labels the correlation coefficients directly on the plot. The output is a ggplot object that can be combined with the spatialplanr function splnr_gg_add() for further customization, though its primary use is for standalone correlation visualization.

Examples

if (FALSE) { # \dontrun{
# Assuming 'dat_species_bin' is an existing sf object in your package.

# Create Problem 1 (30% target) and solve it.
dat_problem <- prioritizr::problem(
  dat_species_bin %>% dplyr::mutate(Cost = runif(n = dim(.)[[1]])),
  features = c("Spp1", "Spp2", "Spp3", "Spp4", "Spp5"),
  cost_column = "Cost"
) %>%
  prioritizr::add_min_set_objective() %>%
  prioritizr::add_relative_targets(0.3) %>%
  prioritizr::add_binary_decisions() %>%
  prioritizr::add_default_solver(verbose = FALSE)

dat_soln <- dat_problem %>%
  prioritizr::solve.ConservationProblem()

# Create Problem 2 (50% target) and solve it.
dat_problem2 <- prioritizr::problem(
  dat_species_bin %>%
    dplyr::mutate(Cost = runif(n = dim(.)[[1]])),
  features = c("Spp1", "Spp2", "Spp3", "Spp4", "Spp5"),
  cost_column = "Cost"
) %>%
  prioritizr::add_min_set_objective() %>%
  prioritizr::add_relative_targets(0.5) %>%
  prioritizr::add_binary_decisions() %>%
  prioritizr::add_default_solver(verbose = FALSE)

dat_soln2 <- dat_problem2 %>%
  prioritizr::solve.ConservationProblem()

# Get the Kappa correlation data for the two solutions.
CorrMat <- splnr_get_kappaCorrData(list(dat_soln, dat_soln2), name_sol = c("soln1", "soln2"))

# Plot the correlation matrix with custom axis labels.
plot_correlation_matrix <- splnr_plot_corrMat(
  CorrMat,
  AxisLabels = c("Solution 1", "Solution 2")
)
print(plot_correlation_matrix)
} # }