Write an Omission / Threshold CSV

Computes cumulative predictions at all background + presence locations and derives nine standard threshold statistics, writing the result to <output_dir>/<species>_omission.csv. The output format mirrors density/Runner.java::writeCumulativeIndex().

Usage

maxent_write_omission_csv(
  model,
  env_grids,
  feature_names,
  presence_rows,
  presence_cols,
  output_dir,
  species,
  test_rows = NULL,
  test_cols = NULL
)

Arguments

model

External pointer to a trained FeaturedSpace object.

env_grids

List of external pointers to Grid<float> objects.

feature_names

Character vector of environment variable names.

presence_rows

Integer vector of presence row indices (0-based).

presence_cols

Integer vector of presence column indices (0-based).

output_dir

Character: directory for output files.

species

Character: species name (used in the filename).

test_rows

Integer vector of test row indices (0-based) or NULL (default).

test_cols

Integer vector of test column indices (0-based) or NULL (default).

Value

Invisibly returns the path to the written CSV file.

Examples

# \donttest{
set.seed(42)
n <- 50L; idx <- c(5L, 15L, 25L, 35L, 45L)
env <- list(bio1 = runif(n), bio12 = runif(n))
feats <- maxent_generate_features(env, types = "linear")
model <- maxent_featured_space(n, idx, feats)
maxent_fit(model, max_iter = 100)
#> $loss
#> [1] 3.781356
#> 
#> $entropy
#> [1] 3.887762
#> 
#> $iterations
#> [1] 100
#> 
#> $converged
#> [1] FALSE
#> 
#> $lambdas
#> [1] 0.6202339 0.4108689
#> 
g1 <- maxent_grid_from_matrix(matrix(env$bio1, 5, 10),
        -120, 35, 1, name = "bio1")
g2 <- maxent_grid_from_matrix(matrix(env$bio12, 5, 10),
        -120, 35, 1, name = "bio12")
pres_rows <- c(0L, 1L, 2L); pres_cols <- c(0L, 1L, 2L)
maxent_write_omission_csv(model, list(g1, g2), c("bio1", "bio12"),
  pres_rows, pres_cols, output_dir = tempdir(), species = "Sp1")
# }