Skip to contents

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

Source: R/cor_gcms.R
cor_gcms.Rd

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).

See also

transform_gcms flatten_gcms summary_gcms

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)
study_area <- terra::ext(c(-80, -30, -50, 10)) |> terra::vect(crs="epsg:4326")
cor_gcms(s, var_names, study_area, method = "pearson")
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
#> $cor_matrix
#>           ae        cc        ch        cr        ev        gg        hg
#> ae 1.0000000 0.9219747 0.9571112 0.9613585 0.9492778 0.9649294 0.9833486
#> cc 0.9219747 1.0000000 0.9695970 0.9644769 0.9719578 0.9560660 0.9366215
#> ch 0.9571112 0.9695970 1.0000000 0.9910983 0.9867315 0.9825975 0.9693717
#> cr 0.9613585 0.9644769 0.9910983 1.0000000 0.9823423 0.9826507 0.9722321
#> ev 0.9492778 0.9719578 0.9867315 0.9823423 1.0000000 0.9773964 0.9546606
#> gg 0.9649294 0.9560660 0.9825975 0.9826507 0.9773964 1.0000000 0.9652498
#> hg 0.9833486 0.9366215 0.9693717 0.9722321 0.9546606 0.9652498 1.0000000
#> in 0.9314944 0.9681631 0.9849882 0.9734534 0.9807411 0.9793463 0.9425552
#> me 0.9556262 0.9642517 0.9886974 0.9832545 0.9796424 0.9783986 0.9652108
#> ml 0.9449743 0.9614685 0.9805508 0.9795914 0.9738266 0.9838261 0.9595722
#> mr 0.9588276 0.9588638 0.9901494 0.9814043 0.9816382 0.9860674 0.9646367
#>           in        me        ml        mr
#> ae 0.9314944 0.9556262 0.9449743 0.9588276
#> cc 0.9681631 0.9642517 0.9614685 0.9588638
#> ch 0.9849882 0.9886974 0.9805508 0.9901494
#> cr 0.9734534 0.9832545 0.9795914 0.9814043
#> ev 0.9807411 0.9796424 0.9738266 0.9816382
#> gg 0.9793463 0.9783986 0.9838261 0.9860674
#> hg 0.9425552 0.9652108 0.9595722 0.9646367
#> in 1.0000000 0.9822258 0.9778581 0.9859656
#> me 0.9822258 1.0000000 0.9806899 0.9883747
#> ml 0.9778581 0.9806899 1.0000000 0.9846096
#> mr 0.9859656 0.9883747 0.9846096 1.0000000
#> 
#> $cor_plot

#>