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Evaluate an SDM

Source: R/evaluate_sdm.R
evaluate_sdm.Rd

Returns various evaluation metrics from predicts::pa_evaluate() and flexsdm::sdm_eval().

Usage

evaluate_sdm(m, p_test, b_test, do_gc = FALSE, ...)

Arguments

m

SDM result within tune_sdm()

p_test

Presence test data generated within tune_sdm()

b_test

Background test data generated within tune_sdm()

do_gc

Logical. Run base::rm(list = ls) and base::gc() at end of function? Useful when running SDMs for many, many taxa, especially if done in parallel. Note, actually usees rm(list = ls(pattern = "^[^e$]")).

...

Passed to both terra::predict() and predicts::pa_evaluate()

Value

paModelEvaluation (see predicts::pa_evaluate()) with extra metrics from flexsdm::sdm_eval(): AUC as auc_po_flexsdm; BOYCE as CBI; CBI_rescale (CBI is -1 to 1, CBI_rescale is 0 to 1); and IMAE.

Examples


out_dir <- file.path(system.file(package = "envSDM"), "examples")

preps <- fs::dir_ls(out_dir, regexp = "prep.rds", recurse = TRUE)

prep <- rio::import(preps[[1]], trust = TRUE)

full_run <- rio::import(fs::path(dirname(preps[[1]]), "full_run.rds"), trust = TRUE)
algo <- full_run$tune_mean$algo[[1]]
model <- full_run[[paste0("tune_", algo)]]$m[[1]]

presences <- prep$testing[prep$testing$pa == 1, ]
background <- prep$testing[prep$testing$pa == 0, ]

evaluate_sdm(full_run$tune_rf$m[[1]]
             , p_test = presences
             , b_test = background
             )
#> @stats
#>   np  na prevalence   auc   cor pcor   ODP auc_po auc_po_flexsdm   CBI
#> 1 27 184      0.128 0.947 0.667    0 0.872  0.947          0.947 0.796
#>   CBI_rescale  IMAE
#> 1       0.898 0.804
#> 
#> @thresholds
#>   max_kappa max_spec_sens no_omission equal_prevalence equal_sens_spec  or10
#> 1     0.676         0.676        0.22            0.132           0.468 0.346
#> 
#> @tr_stats
#>     treshold kappa  CCR  TPR  TNR  FPR  FNR  PPP  NPP  MCR    OR
#> 1          0     0 0.13    1    0    1    0 0.13  NaN 0.87   NaN
#> 2          0     0 0.13    1 0.01 0.99    0 0.13    1 0.87   Inf
#> 3          0  0.01 0.17    1 0.04 0.96    0 0.13    1 0.83   Inf
#> 4        ...   ...  ...  ...  ...  ...  ...  ...  ...  ...   ...
#> 129        1  0.22 0.89 0.15 0.99 0.01 0.85  0.8 0.89 0.11 31.83
#> 130        1  0.22 0.89 0.15 0.99 0.01 0.85  0.8 0.89 0.11 31.83
#> 131        1     0 0.87    0    1    0    1  NaN 0.87 0.13   NaN