Prepare data for model calibration

This function prepares data for model calibration, including optional PCA, background point generation, training/testing partitioning, and the creation of a grid of parameter combinations, including regularization multiplier values, feature classes, and sets of environmental variables.

Usage

prepare_data(algorithm, occ, x, y, raster_variables, species = NULL,
             n_background = 1000, features = c("lq", "lqp"),
             r_multiplier = c(0.1, 0.5, 1, 2, 3),
             user_formulas = NULL,
             partition_method = "kfolds",
             n_partitions = 4, train_proportion = 0.7,
             categorical_variables = NULL,
             do_pca = FALSE, center = TRUE, scale = TRUE,
             exclude_from_pca = NULL, variance_explained = 95,
             min_explained = 5, min_number = 2, min_continuous = NULL,
             bias_file = NULL, bias_effect = NULL, weights = NULL,
             include_xy = TRUE, write_pca = FALSE, pca_directory = NULL,
             write_file = FALSE, file_name = NULL, seed = 1)

Arguments

algorithm: (character) modeling algorithm, either "glm" or "maxnet".
occ: (data frame) a data.frame containing the coordinates (longitude and latitude) of the occurrence records.
x: (character) a string specifying the name of the column in occ that contains the longitude values.
y: (character) a string specifying the name of the column in occ that contains the latitude values.
raster_variables: (SpatRaster) predictor variables from which environmental values will be extracted using occ and a background will be sampled. Must correspond geographically with the area where model is calibrated.
species: (character) string specifying the species name (optional). Default is NULL.
n_background: (numeric) number of points to represent the background for the model. Default is 1000.
features: (character) a vector of feature classes. Default is c("q", "lq", "lp", "qp", "lqp").
r_multiplier: (numeric) a vector of regularization parameters for maxnet. Default is c(0.1, 1, 2, 3, 5).
user_formulas: (character) Optional character vector with custom formulas provided by the user. See Details. Default is NULL.
partition_method: (character) method used for data partitioning. Available options are "kfolds", "subsample", and "bootstrap". See Details for more information. Default = "kfolds".
n_partitions: (numeric) number of partitions to generate. If partition_method is "subsample" or "bootstrap", this defines the number of training testing replicates. If "kfolds", it specifies the number of folds. Must be > 1; default = 4.
train_proportion: (numeric) proportion of occurrence and background points to be used for model training in each partition. Only applicable when partition_method is "subsample" or "bootstrap". Default is 0.7 (i.e., 70% for training and 30% for testing).
categorical_variables: (character) names of the variables that are categorical. Default is NULL.
do_pca: (logical) whether to perform a principal component analysis (PCA) with the set of variables. Default is FALSE.
center: (logical) whether the variables should be zero-centered. Default is TRUE.
scale: (logical) whether the variables should be scaled to have unit variance before the analysis takes place. Default is FALSE.
exclude_from_pca: (character) variable names within raster_variables that should not be included in the PCA transformation. Instead, these variables will be added directly to the final set of output variables without being modified. The default is NULL, meaning all variables will be used unless specified otherwise.
variance_explained: (numeric) the cumulative percentage of total variance that must be explained by the selected principal components. Default is 95.
min_explained: (numeric) the minimum percentage of total variance that a principal component must explain to be retained. Default is 5.
min_number: (numeric) the minimum number of variables to be included in model formulas to be generated. Default = 2.
min_continuous: (numeric) the minimum number of continuous variables required in a combination. Default is NULL.
bias_file: (SpatRaster) a raster containing bias values (probability weights) that influence the selection of background points. It must have the same extent, resolution, and number of cells as the raster variables. Default is NULL.
bias_effect: (character) a string specifying how the values in the bias_file should be interpreted. Options are "direct" or "inverse". If "direct", higher values in bias file increase the likelihood of selecting background points. If "inverse", higher values decrease the likelihood. Default = NULL. Must be defined if bias_file is provided.
weights: (numeric) a numeric vector specifying weights for the occurrence records. The default, NULL, uses 1 for presence and 100 for background.
include_xy: (logical) whether to include the coordinates (longitude and latitude) in the results from preparing data. Columns containing coordinates will be renamed as "x" and "y". Default is TRUE.
write_pca: (logical) whether to save the PCA-derived raster layers (principal components) to disk. Default is FALSE.
pca_directory: (character) the path or name of the folder where the PC raster layers will be saved. This is only applicable if write_pca = TRUE. Default is NULL.
write_file: (logical) whether to write the resulting prepared_data list in a local directory. Default is FALSE.
file_name: (character) name of file (no extension needed) to write resulting object in a local directory. Only needed if write_file = TRUE. Default is NULL.
seed: (numeric) integer value to specify an initial seed to split the data and extract background. Default is 1.

Value

An object of class prepared_data containing all elements necessary to perform further explorations of data and run a model calibration routine.

Details

Training and testing are performed multiple times (i.e., the number set in n_partitions), and model selection is based on the average performance of models after running this routine. A description of the available data partitioning methods is below:

"kfolds": Splits the dataset into K subsets (folds) of approximately equal size, keeping proportion of 0 and 1 stable compared to the full set. In each training/test run, one fold is used as the test set, while the remaining folds are combined to form the training set.
"bootstrap": Creates the training dataset by sampling observations from the original dataset with replacement (i.e., the same observation can be selected multiple times). The test set consists of the observations that were not selected in that specific sampling.
"subsample": Similar to bootstrap, but the training set is created by sampling without replacement (i.e., each observation is selected at most once).

user_formulas must be a character vector of model formulas. Supported terms include linear effects, quadratic terms (e.g., I(bio_7^2)), products (e.g., bio_1:bio_7), hinge (e.g., hinge(bio_1)), threshold (e.g., thresholds(bio_2)), and categorical predictors (e.g., categorical(SoilType)). Example of a valid formula: ~ bio_1 + bio_7 + I(bio_7^2) + bio_1:bio_7 + hinge(bio_1) + thresholds(bio_2) + categorical(SoilType). All variables appearing in the formulas must exist in the raster supplied as raster_variables.

Examples

# Import occurrences
data(occ_data, package = "kuenm2")

# Import raster layers
var <- terra::rast(system.file("extdata", "Current_variables.tif",
                               package = "kuenm2"))

# Import a bias file
bias <- terra::rast(system.file("extdata", "bias_file.tif",
                                package = "kuenm2"))

# Prepare data for maxnet model
sp_swd <- prepare_data(algorithm = "maxnet", occ = occ_data,
                       x = "x", y = "y",
                       raster_variables = var,
                       species = occ_data[1, 1],
                       categorical_variables = "SoilType",
                       n_background = 500, bias_file = bias,
                       bias_effect = "direct",
                       features = c("l", "q", "p", "lq", "lqp"),
                       r_multiplier = c(0.1, 1, 2, 3, 5))
#> Warning: 27 rows were excluded from database because NAs were found.
print(sp_swd)
#> prepared_data object summary
#> ============================
#> Species: Myrcia hatschbachii 
#> Number of Records: 524 
#>   - Presence: 51 
#>   - Background: 473 
#> Partition Method: kfolds 
#>   - Number of kfolds: 4 
#> Continuous Variables:
#>   - bio_1, bio_7, bio_12, bio_15 
#> Categorical Variables:
#>   - SoilType 
#> PCA Information: PCA not performed
#> Weights: No weights provided
#> Calibration Parameters:
#>   - Algorithm: maxnet 
#>   - Number of candidate models: 610 
#>   - Features classes (responses): l, q, p, lq, lqp 
#>   - Regularization multipliers: 0.1, 1, 2, 3, 5 

# Prepare data for glm model
sp_swd_glm <- prepare_data(algorithm = "glm", occ = occ_data,
                           x = "x", y = "y",
                           raster_variables = var,
                           species = occ_data[1, 1],
                           categorical_variables = "SoilType",
                           n_background = 500, bias_file = bias,
                           bias_effect = "direct",
                           features = c("l", "q", "p", "lq", "lqp"))
#> Warning: 27 rows were excluded from database because NAs were found.
print(sp_swd_glm)
#> prepared_data object summary
#> ============================
#> Species: Myrcia hatschbachii 
#> Number of Records: 524 
#>   - Presence: 51 
#>   - Background: 473 
#> Partition Method: kfolds 
#>   - Number of kfolds: 4 
#> Continuous Variables:
#>   - bio_1, bio_7, bio_12, bio_15 
#> Categorical Variables:
#>   - SoilType 
#> PCA Information: PCA not performed
#> Weights: No weights provided
#> Calibration Parameters:
#>   - Algorithm: glm 
#>   - Number of candidate models: 122 
#>   - Features classes (responses): l, q, p, lq, lqp

Usage

Arguments

Value

Details

See also

Examples