glossa_analysis {glossa} | 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,
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
)
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''). |
model_args |
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. |
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.