This function wraps all the analysis that the GLOSSA package performs. It processes presence-absence data, environmental covariates, and performs species distribution modeling and projections under past and future scenarios.
glossa_analysis(
pa_data = NULL,
fit_layers = NULL,
proj_files = NULL,
study_area_poly = NULL,
predictor_variables = NULL,
thinning_method = NULL,
thinning_value = NULL,
scale_layers = FALSE,
buffer = NULL,
native_range = NULL,
suitable_habitat = NULL,
other_analysis = NULL,
model_args = list(),
cv_methods = NULL,
cv_folds = 5,
cv_block_source = "residuals_autocorrelation",
cv_block_size = NULL,
pseudoabsence_method = "random",
pa_ratio = 1,
target_group_points = NULL,
pa_buffer_distance = NULL,
seed = NA,
waiter = NULL
)A list containing structured outputs from each major section of the analysis, including model data, projections, variable importance scores, and habitat suitability assessments.
A list of data frames containing presence-absence data including `decimalLongitude`, `decimalLatitude`, `timestamp`, and `pa` columns.
A ZIP file with the raster files containing model fitting environmental layers formatted as explained in the website documentation.
A list of ZIP file paths containing environmental layers for projection scenarios.
A spatial polygon defining the study area.
A list of the predictor variables to be used in the analysis for each occurrence dataset.
A character specifying the spatial thinning method to apply to occurrence data. Options are `c("none", "distance", "grid", "precision")`. See `GeoThinneR` package for details.
A numeric value used for thinning depending on the selected method: distance in meters (`distance`), grid resolution in degrees (`grid`), or decimal precision (`precision`).
Logical; if `TRUE`, covariate layers will be standardize (z-score) based on fit layers.
Buffer value or distance in decimal degrees (arc_degrees) for buffering the study area polygon.
A vector of scenarios `c('fit_layers', 'projections')` where native range modeling should be performed.
A vector of scenarios `c('fit_layers', 'projections')` where habitat suitability modeling should be performed.
A vector of additional analyses to perform (e.g., `'variable_importance', 'functional_responses', 'cross_validation'`).
A named list of additional arguments passed to the modeling function (e.g., `dbarts::bart`). This allows users to fine-tune model parameters such as `ntree` or `k`. These are passed internally via `...` and must match the arguments of the selected model function.
A vector of the cross-validation strategies to perform. One or multiple of `"k-fold"`, `"spatial_blocks"`, `"temporal_blocks"`.
Integer indicating the number of folds to generate.
For spatial blocks, how to determine block size. One of: `"residuals_autocorrelation"`, `"predictors_autocorrelation"`, `"manual"`.
Numeric block size in meters (used if `cv_block_source = "manual"`).
Method for generating pseudo-absences. One of "random", "target_group", or "buffer_out".
Ratio of pseudo-absences to presences (pseudo-absence:presences).
Optional data frame for sampling points for target-group method.
Numeric buffer radius in degrees around each presence. Default is NULL.
Optional; an integer seed for reproducibility of results.
Optional; a waiter instance to update progress in a Shiny application.