Compute and Plot Correlation Matrix for a Set of General Circulation Models

This function computes and visualizes the correlation matrix for a set of General Circulation Models (GCMs) based on their variables.

Usage

cor_gcms(
  s,
  var_names = c("bio_1", "bio_12"),
  study_area = NULL,
  scale = TRUE,
  method = "pearson"
)

Arguments

s: A list of stacks of General Circulation Models (GCMs).
var_names: Character. A vector with names of the variables to compare, or 'all' to include all variables.
study_area: An Extent object, or any object from which an Extent object can be extracted. Defines the study area for cropping and masking the rasters.
scale: Logical. Whether to apply centering and scaling to the data. Default is TRUE.
method: Character. The correlation method to use. Default is "pearson". Possible values are: "pearson", "kendall", or "spearman".

Value

A list containing two items: cor_matrix (the calculated correlations between GCMs) and cor_plot (a plot visualizing the correlation matrix).

Author

Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com

Examples

var_names <- c("bio_1", "bio_12")
s <- import_gcms(system.file("extdata", package = "chooseGCM"), var_names = var_names)[1:5]
study_area <- terra::ext(c(-80, -70, -50, -40)) |>
  terra::vect(crs="+proj=longlat +datum=WGS84 +no_defs")
cor_gcms(s, var_names, study_area, method = "pearson")
#> CRS from s and study_area are not identical. Reprojecting study area.
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
#> $cor_matrix
#>           ae        cc        ch        cr        ev
#> ae 1.0000000 0.9978482 0.9692338 0.9735887 0.9874646
#> cc 0.9978482 1.0000000 0.9632915 0.9684004 0.9804430
#> ch 0.9692338 0.9632915 1.0000000 0.9994263 0.9934712
#> cr 0.9735887 0.9684004 0.9994263 1.0000000 0.9942620
#> ev 0.9874646 0.9804430 0.9934712 0.9942620 1.0000000
#> 
#> $cor_plot

#>