Correlation between projections

This function aims to unveil the correlation of different algorithms outputs. For that, it uses the predictions on current scenario, but other scenarios can be tested.

Usage

correlate_sdm(i, scenario = "current")

Arguments

i

A input_sdm object containing predictions.

scenario

A character containing scenario to be tested. Standard is "current". Value must match scenarios_names(i).

Value

A data.frame with pearson correlation between projections.

Author

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

Examples

# Create sdm_area object:
set.seed(1)
sa <- sdm_area(parana, cell_size = 100000, crs = 6933)
#> ! Making grid over study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Include predictors:
sa <- add_predictors(sa, bioc) |> select_predictors(c("bio1", "bio12"))
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Include scenarios:
sa <- add_scenarios(sa)

# Create occurrences:
oc <- occurrences_sdm(occ, crs = 6933) |> join_area(sa)

# Create input_sdm:
i <- input_sdm(oc, sa)

# Pseudoabsence generation:
i <- pseudoabsences(i, method="random", n_set=2)

# Custom trainControl:
ctrl_sdm <- caret::trainControl(method = "boot",
                                number = 1,
                                repeats = 1,
                                classProbs = TRUE,
                                returnResamp = "all",
                                summaryFunction = summary_sdm,
                                savePredictions = "all")
#> Warning: `repeats` has no meaning for this resampling method.

# Train models:
i <- train_sdm(i, algo = c("naive_bayes"), ctrl=ctrl_sdm) |>
  suppressWarnings()
#> Loading required package: ggplot2
#> Loading required package: lattice
#> 
#> Attaching package: ‘caret’
#> The following object is masked from ‘package:caretSDM’:
#> 
#>     predictors

# Predict models:
i  <- predict_sdm(i, th = 0.8)
#> [1] "Projecting: 1/1"
#> [1] "Ensembling..."
#> [1] "current"
#> [1] "Araucaria angustifolia"

# Check correlations:
correlate_sdm(i)
#> $`Araucaria angustifolia`
#>      m1.1 m2.1
#> m1.1    1    1
#> m2.1    1    1
#>