Package index • ibis.iSDM

Setting up and train models

Key functions for setting up species distribution models and adding information to them. Start with distribution() and see articles examples on how to build a model from there. Also includes functions to specify priors for a model.

distribution(): Create distribution modelling procedure

BARTPrior(): Create a tree-based split probability prior for BART

BARTPriors(): Helper function when multiple variables are supplied for a BART prior

BREGPrior(): Create a new spike and slab prior for Bayesian generalized linear models

BREGPriors(): Helper function when multiple variables are supplied for a BREG prior

GDBPrior(): Monotonic constrained priors for boosted regressions

GDBPriors(): Helper function when multiple variables are supplied for a GDB prior

GLMNETPrior(): Regression penalty priors for GLMNET

GLMNETPriors(): Helper function when multiple variables are supplied for a GLMNET prior

INLAPrior(): Create a new INLA prior

INLAPriors(): Helper function when multiple variables and types are supplied for INLA

STANPrior(): Create a new STAN prior

STANPriors(): Helper function when multiple variables and types are supplied for STAN

XGBPrior(): Create a new monotonic prior for boosted regressions

XGBPriors(): Helper function when multiple variables are supplied for XGBOOST

Prior-class Prior: Base Prior class

PriorList-class PriorList: List of Priors supplied to an class

print(<distribution>) print(<BiodiversityDistribution>) print(<BiodiversityDatasetCollection>) print(<BiodiversityDataset>) print(<PredictorDataset>) print(<DistributionModel>) print(<BiodiversityScenario>) print(<Prior>) print(<PriorList>) print(<Engine>) print(<Settings>) print(<Log>) print(<Id>) print(<tbl_df>): Print

summary(<distribution>) summary(<DistributionModel>) summary(<PredictorDataset>) summary(<BiodiversityScenario>) summary(<PriorList>) summary(<Settings>): Summarises a trained model or predictor object

priors(): Creates a new PriorList object

pseudoabs_settings(): Settings for specifying pseudo-absence points within the model background

train(): Train the model from a given engine

Add or modify data and parameters

Functions to add or modify data and parameters in a distribution object. These can be used to add or remove biodiversity, covariates and priors in various forms.

add_biodiversity_poipa(): Add biodiversity point dataset to a distribution object (presence-absence).

add_biodiversity_poipo(): Add biodiversity point dataset to a distribution object (presence-only)

add_biodiversity_polpa(): Add biodiversity polygon dataset to a distribution object (presence-absence)

add_biodiversity_polpo(): Add biodiversity polygon dataset to a distribution object (presence-only)

add_constraint(): Add a constraint to an existing scenario

add_constraint_MigClim(): Add constrains to the modelled distribution projection using the MigClim approach

add_constraint_adaptability(): Adds an adaptability constraint to a scenario object

add_constraint_boundary(): Adds a boundary constraint to a scenario object

add_constraint_connectivity(): Adds a connectivity constraint to a scenario object.

add_constraint_dispersal(): Add dispersal constraint to an existing scenario

add_constraint_minsize(): Adds a size constraint on a scenario

add_constraint_threshold(): Adds a threshold constraint to a scenario object

add_control_bias(): Add a control to a BiodiversityModel object to control biases

add_latent_spatial(): Add latent spatial effect to the model equation

add_limits_extrapolation(): Add a control to a BiodiversityModel object to limit extrapolation

add_log(): Adds a log file to distribution object

add_offset(): Specify a spatial explicit offset

add_offset_bias(): Specify a spatial explicit offset as bias

add_offset_elevation(): Specify elevational preferences as offset

add_offset_range(): Specify a expert-based species range as offset

add_predictor_elevationpref(): Create lower and upper limits for an elevational range and add them as separate predictors

add_predictor_range(): Add a range of a species as predictor to a distribution object

add_predictors(): Add predictors to a Biodiversity distribution object

add_predictors_globiom(): Function to add GLOBIOM-DownScalr derived predictors to a Biodiversity distribution object

add_predictors_model(): Add predictions from a fitted model to a Biodiversity distribution object

add_priors(): Add priors to an existing distribution object

add_pseudoabsence(): Add pseudo-absence points to a point data set

set_priors(<BiodiversityDistribution>): Add priors to an existing distribution object

set_priors(): Add priors to an existing distribution object

sel_predictors(): Select specific predictors from a distribution object

rm_biodiversity(): Remove specific BiodiversityDataset from a distribution object

rm_control(): Remove control from an existing distribution object

rm_latent(): Function to remove a latent effect

rm_limits(): Remove limits from an existing distribution object

rm_offset(): Function to remove an offset

rm_predictors(): Remove specific predictors from a distribution object

rm_priors(): Remove existing priors from an existing distribution object

get_data(): Small helper function to obtain predictions from an object

get_ngbvalue(): Function to extract nearest neighbour predictor values of provided points

get_priors(): Create priors from an existing distribution model

get_rastervalue(): Function to extract point values directly from a SpatRaster

Engines

Statistical models used for estimation of species distributions.

engine_bart(): Engine for use of Bayesian Additive Regression Trees (BART)

engine_breg(): Engine for Bayesian regularized regression models

engine_gdb(): Use of Gradient Descent Boosting for model estimation

engine_glm(): Engine for Generalized linear models (GLM)

engine_glmnet(): Engine for regularized regression models

engine_inla(): Use INLA as engine

engine_inlabru(): Use inlabru as engine

engine_scampr(): Engine for process models using scampr

engine_stan(): Use Stan as engine

engine_xgboost(): Engine for extreme gradient boosting (XGBoost)

Create spatial-temporal projections

After a model has been trained, the functions in here can be used to create projections with scenario() objects. Constraints can be on such scenarios to limit extrapolations.

scenario(): Create a new scenario based on trained model parameters

project.BiodiversityScenario() project(<BiodiversityScenario>) project.DistributionModel() project(<DistributionModel>): Project a fitted model to a new environment and covariates

simulate_population_steps(): Simulate population dynamics following the steps approach

add_constraint(): Add a constraint to an existing scenario

add_constraint_MigClim(): Add constrains to the modelled distribution projection using the MigClim approach

add_constraint_adaptability(): Adds an adaptability constraint to a scenario object

add_constraint_boundary(): Adds a boundary constraint to a scenario object

add_constraint_connectivity(): Adds a connectivity constraint to a scenario object.

add_constraint_dispersal(): Add dispersal constraint to an existing scenario

add_constraint_minsize(): Adds a size constraint on a scenario

add_constraint_threshold(): Adds a threshold constraint to a scenario object

Model summary and validation

Key functions to summarize, validate or extract information from trained models.

plot(<DistributionModel>) plot(<BiodiversityDatasetCollection>) plot(<PredictorDataset>) plot(<Engine>) plot(<BiodiversityScenario>): Plot wrappers

bivplot(): Bivariate prediction plot for distribution objects

nicheplot(): Niche plot for distribution objects

print(<distribution>) print(<BiodiversityDistribution>) print(<BiodiversityDatasetCollection>) print(<BiodiversityDataset>) print(<PredictorDataset>) print(<DistributionModel>) print(<BiodiversityScenario>) print(<Prior>) print(<PriorList>) print(<Engine>) print(<Settings>) print(<Log>) print(<Id>) print(<tbl_df>): Print

summary(<distribution>) summary(<DistributionModel>) summary(<PredictorDataset>) summary(<BiodiversityScenario>) summary(<PriorList>) summary(<Settings>): Summarises a trained model or predictor object

coef(<DistributionModel>): Obtains the coefficients of a trained model

validate(): Validation of a fitted distribution object

similarity(): Calculate environmental similarity of reference datasets to predictors.

effects(<DistributionModel>): Plot effects of trained model

partial() partial.DistributionModel(): Obtain partial effects of trained model

spartial() spartial.DistributionModel(): Obtain spatial partial effects of trained model

partial_density(): Visualize the density of the data over the environmental data

limiting(): Identify local limiting factor

threshold(): Threshold a continuous prediction to a categorical layer

ensemble(): Function to create an ensemble of multiple fitted models

ensemble_partial(): Function to create an ensemble of partial effects from multiple models

ensemble_spartial(): Function to create an ensemble of spartial effects from multiple models

Utility functions

These functions are used by engines or spatial processing in the package. Most of them are for internal use, but can be of use if input needs to be reformatted.

posterior_predict_stanfit(): Create a posterior prediction from a rstanfit object

alignRasters(): Align a SpatRaster object to another by harmonizing geometry and extend.

emptyraster(): Create an empty SpatRaster based on a template

get_ngbvalue(): Function to extract nearest neighbour predictor values of provided points

get_rastervalue(): Function to extract point values directly from a SpatRaster

predictor_transform(): Spatial adjustment of environmental predictors and raster stacks

predictor_derivate(): Create spatial derivative of raster stacks

predictor_filter(): Filter a set of correlated predictors to fewer ones

predictor_summarize_zones(): Summarize and replace values in predictors with an aggregation

interpolate_gaps(): Approximate missing time steps between dates

run_stan(): Fit cmdstanr model and convert to rstan object

wrap_stanmodel(): Wrap a list with stan model code

sanitize_names(): Sanitize variable names

get_data(): Small helper function to obtain predictions from an object

combine_formulas(): Combine or concatenate multiple formula objects

stancode() stancode.DistributionModel(): Show the stan code from a trained model

write_model(): Save a model for later use

write_output(): Generic function to write spatial outputs

write_summary(): Generic function to write summary outputs from created models.

load_model(): Load a pre-computed model

mask.DistributionModel() mask.BiodiversityDatasetCollection() mask.PredictorDataset() mask.BiodiversityScenario(): Mask data with an external layer

myLog(): Custom messaging function for scripts

predictor_homogenize_na(): Homogenize NA values across a set of predictors.

run_parallel(): Parallel computation of function

thin_observations(): Functionality for geographic and environmental thinning

unwrap_model(): Unwrap a model for later use

wrap_model(): Wrap a model for later use

Class definitions and methods

These pages document the package’s internal data structures and functions for manipulating them—they contain information that is really only useful when adding new functionality to the package.

BiodiversityDataset-class BiodiversityDataset: BiodiversityDataset prototype description

BiodiversityDatasetCollection-class BiodiversityDatasetCollection: BiodiversityDatasetCollection super class description

BiodiversityDistribution-class BiodiversityDistribution: Biodiversity Distribution master class

BiodiversityScenario-class BiodiversityScenario: Class for a biodiversity scenario from a trained model

DistributionModel-class DistributionModel: Class for the trained Model object

Engine-class Engine: Engine class description

Prior-class Prior: Base Prior class

PriorList-class PriorList: List of Priors supplied to an class

Settings-class Settings: Prototype for model settings object

PredictorDataset-class PredictorDataset: PredictorDataset class description

Log-class Log: Log prototype.

Parallel processing

Functions to enable or set up parallel processing

ibis_future(): Internal function to enable (a)synchronous parallel processing

ibis_enable_parallel(): Set the parallel processing flag to TRUE

ibis_set_strategy(): Set the number of threads for parallel processing.

ibis_set_threads(): Set the threads for parallel processing.

run_parallel(): Parallel computation of function

Miscellaneous functions

Other functions only relevant for development

as.Id(): As Id

is.Id(): Check whether a provided object is truly of a specific type

check(): Check objects in the package for common errors or issues

bivplot(): Bivariate prediction plot for distribution objects

ibis_dependencies(): Install ibis dependencies

ibis_enable_parallel(): Set the parallel processing flag to TRUE

ibis_future(): Internal function to enable (a)synchronous parallel processing

ibis_options(): Print ibis options

ibis_set_strategy(): Set the number of threads for parallel processing.

ibis_set_threads(): Set the threads for parallel processing.

is.Raster(): Tests if an input is a SpatRaster object.

is.Waiver(): Is the provided object of type waiver?

is.formula(): Check whether a formula is valid

is.stars(): Tests if an input is a stars object.

modal(): Calculate the mode of a provided vector

new_id(): Identifier

new_waiver(): Waiver

nicheplot(): Niche plot for distribution objects

plot(<DistributionModel>) plot(<BiodiversityDatasetCollection>) plot(<PredictorDataset>) plot(<Engine>) plot(<BiodiversityScenario>): Plot wrappers

print(<distribution>) print(<BiodiversityDistribution>) print(<BiodiversityDatasetCollection>) print(<BiodiversityDataset>) print(<PredictorDataset>) print(<DistributionModel>) print(<BiodiversityScenario>) print(<Prior>) print(<PriorList>) print(<Engine>) print(<Settings>) print(<Log>) print(<Id>) print(<tbl_df>): Print

render_html(): render_html

run_stan(): Fit cmdstanr model and convert to rstan object

myLog(): Custom messaging function for scripts