tenm (version 0.5.1)

tenm_selection: Function to find the best n-dimensional ellipsoid model

Description

Finds the best n-dimensional ellipsoid model using a model calibration and selection protocol for ellipsoid models.

Usage

tenm_selection(
  this_species,
  omr_criteria = 0.1,
  ellipsoid_level = 0.975,
  vars2fit,
  nvars_to_fit = c(2, 3),
  mve = TRUE,
  proc = TRUE,
  sub_sample = TRUE,
  sub_sample_size = 1000,
  RandomPercent = 50,
  NoOfIteration = 1000,
  parallel = TRUE,
  n_cores = 4
)

Value

An object of class "sp.temporal.selection" containing metadata of model statistics of the calibrated models, obtainable from the "mods_table" attribute. The function internally uses ellipsoid_selection

to obtain model statistics. Note that this function inherits attributes from classes "sp.temporal.modeling" (see sp_temporal_data), "sp.temporal.env" (see ex_by_date), and "sp.temporal.bg" (see bg_by_date), thus all information from these classes can be extracted from this object.

Arguments

this_species

An object of class sp.temporal.env representing species occurrence data organized by date. See ex_by_date.

omr_criteria

Omission rate criterion used to select the best models. See ellipsoid_selection for details

ellipsoid_level

Proportion of points to include inside the ellipsoid.

vars2fit

A vector of variable names to use in building the models.

nvars_to_fit

Number of variables used to build the models.

mve

Logical. If TRUE, a minimum volume ellipsoid will be computed.

proc

Logical. If TRUE, compute the partial ROC test for each model.

sub_sample

Logical. Indicates whether to use a subsample of size sub_sample_size for computing pROC values, recommended for large datasets.

sub_sample_size

Size of the sub_sample to use for computing pROC values when sub_sample is TRUE.

RandomPercent

Percentage of occurrence points to sample randomly for bootstrap in the Partial ROC test. See pROC.

NoOfIteration

Number of iterations for the bootstrap in the Partial ROC test. See pROC.

parallel

Logical. Whether to run computations in parallel. Default is TRUE.

n_cores

Number of cores to use for parallelization. Default is 4.

Examples

# \donttest{
library(tenm)
data("abronia")
tempora_layers_dir <- system.file("extdata/bio",package = "tenm")
abt <- tenm::sp_temporal_data(occs = abronia,
                              longitude = "decimalLongitude",
                              latitude = "decimalLatitude",
                              sp_date_var = "year",
                              occ_date_format="y",
                              layers_date_format= "y",
                              layers_by_date_dir = tempora_layers_dir,
                              layers_ext="*.tif$")
abtc <- tenm::clean_dup_by_date(abt,threshold = 10/60)
future::plan("multisession",workers=2)
abex <- tenm::ex_by_date(this_species = abtc,
                         train_prop=0.7)
abbg <- tenm::bg_by_date(abex,
                         buffer_ngbs=10,n_bg=50000)
future::plan("sequential")
varcorrs <- tenm::correlation_finder(environmental_data = abex$env_data[,-ncol(abex$env_data)],
                                     method = "spearman",
                                     threshold = 0.8,
                                     verbose = FALSE)
vars2fit <- varcorrs$descriptors
mod_sel <- tenm::tenm_selection(this_species = abbg,
                                omr_criteria =0.1,
                                ellipsoid_level=0.975,
                                vars2fit = vars2fit,
                                nvars_to_fit=c(2,3),
                                proc = TRUE,
                                RandomPercent = 50,
                                NoOfIteration=1000,
                                parallel=TRUE,
                                n_cores=20)
# Project potential distribution using bioclimatic layers for 1970-2000
# period.
layers_70_00_dir <- system.file("extdata/bio_1970_2000",package = "tenm")
suit_1970_2000 <- predict(mod_sel,model_variables = NULL,
                          layers_path = layers_70_00_dir,
                          layers_ext = ".tif$")
terra::plot(suit_1970_2000)
colors <- c('#000004FF', '#040312FF', '#0B0725FF',
            '#0B0725FF', '#160B38FF', '#160B38FF',
            '#230C4CFF', '#310A5CFF', '#3F0966FF',
            '#4D0D6CFF', '#5A116EFF', '#67166EFF',
            '#741A6EFF', '#81206CFF', '#81206CFF',
            '#8E2469FF', '#9B2964FF', '#A82E5FFF',
            '#B53359FF', '#B53359FF', '#C03A50FF',
            '#CC4248FF', '#D74B3FFF', '#E05536FF',
            '#E9602CFF', '#EF6E21FF', '#F57B17FF',
            '#F8890CFF', '#FB9806FF', '#FB9806FF',
            '#FCA70DFF', '#FBB81DFF', '#F9C72FFF',
            '#F9C72FFF', '#F6D847FF', '#F2E763FF',
            '#F2E763FF', '#F3F585FF', '#FCFFA4FF',
            '#FCFFA4FF')
points(abtc$temporal_df[,1:2],pch=17,cex=1,
       col=rev(colors))
legend("topleft",legend = abtc$temporal_df$year[1:18],
       col =rev(colors[1:18]),
       cex=0.75,pch=17)
legend("topright",legend = unique(abtc$temporal_df$year[19:40]),
       col = rev(colors[19:40]),
       cex=0.75,pch=17)
# }