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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 97 184      0.345 0.992 0.917    0 0.655  0.992          0.992 0.955
#>   CBI_rescale  IMAE
#> 1       0.978 0.912
#> 
#> @thresholds
#>   max_kappa max_spec_sens no_omission equal_prevalence equal_sens_spec or10
#> 1     0.714         0.714       0.128            0.352           0.714 0.98
#> 
#> @tr_stats
#>    treshold kappa  CCR  TPR  TNR  FPR  FNR  PPP  NPP  MCR  OR
#> 1         0     0 0.35    1    0    1    0 0.35  NaN 0.65 NaN
#> 2         0  0.12 0.45    1 0.16 0.84    0 0.39    1 0.55 Inf
#> 3         0   0.2 0.52    1 0.27 0.73    0 0.42    1 0.48 Inf
#> 4       ...   ...  ...  ...  ...  ...  ...  ...  ...  ... ...
#> 95        1  0.83 0.93 0.79    1    0 0.21    1  0.9 0.07 Inf
#> 96        1  0.83 0.93 0.79    1    0 0.21    1  0.9 0.07 Inf
#> 97        1     0 0.65    0    1    0    1  NaN 0.65 0.35 NaN