glossa_analysis: Main Analysis Function for GLOSSA Package
In glossa: User-Friendly 'shiny' App for Bayesian Species Distribution Models

glossa_analysis

R Documentation

Main Analysis Function for GLOSSA Package

Description

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.

Usage

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,
  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
)

Arguments

`pa_data`	A list of data frames containing presence-absence data including 'decimalLongitude', 'decimalLatitude', 'timestamp', and 'pa' columns.
`fit_layers`	A ZIP file with the raster files containing model fitting environmental layers formatted as explained in the website documentation.
`proj_files`	A list of ZIP file paths containing environmental layers for projection scenarios.
`study_area_poly`	A spatial polygon defining the study area.
`predictor_variables`	A list of the predictor variables to be used in the analysis for each occurrence dataset.
`thinning_method`	A character specifying the spatial thinning method to apply to occurrence data. Options are 'c("none", "distance", "grid", "precision")'. See 'GeoThinneR' package for details.
`thinning_value`	A numeric value used for thinning depending on the selected method: distance in meters ('distance'), grid resolution in degrees ('grid'), or decimal precision ('precision').
`scale_layers`	Logical; if 'TRUE', covariate layers will be standardize (z-score) based on fit layers.
`buffer`	Buffer value or distance in decimal degrees (arc_degrees) for buffering the study area polygon.
`native_range`	A vector of scenarios ‘c(’fit_layers', 'projections')' where native range modeling should be performed.
`suitable_habitat`	A vector of scenarios ‘c(’fit_layers', 'projections')' where habitat suitability modeling should be performed.
`other_analysis`	A vector of additional analyses to perform (e.g., ''variable_importance', 'functional_responses', 'cross_validation'').
`cv_methods`	A vector of the cross-validation strategies to perform. One or multiple of '"k-fold"', '"spatial_blocks"', '"temporal_blocks"'.
`cv_folds`	Integer indicating the number of folds to generate.
`cv_block_source`	For spatial blocks, how to determine block size. One of: '"residuals_autocorrelation"', '"predictors_autocorrelation"', '"manual"'.
`cv_block_size`	Numeric block size in meters (used if 'cv_block_source = "manual"').
`pseudoabsence_method`	Method for generating pseudo-absences. One of "random", "target_group", or "buffer_out".
`pa_ratio`	Ratio of pseudo-absences to presences (pseudo-absence:presences).
`target_group_points`	Optional data frame for sampling points for target-group method.
`pa_buffer_distance`	Numeric buffer radius in degrees around each presence. Default is NULL.
`seed`	Optional; an integer seed for reproducibility of results.
`waiter`	Optional; a waiter instance to update progress in a Shiny application.

Value

A list containing structured outputs from each major section of the analysis, including model data, projections, variable importance scores, and habitat suitability assessments.

glossa documentation built on June 8, 2025, 1:20 p.m.