Fit and validate Extreme Gradient Boosting models with exploration of hyper-parameters that optimize performance

Usage

tune_abund_xgb(
  data,
  response,
  predictors,
  predictors_f = NULL,
  partition,
  predict_part = FALSE,
  grid = NULL,
  objective = "reg:squarederror",
  metrics = NULL,
  n_cores = 1,
  verbose = TRUE
)

Arguments

data: tibble or data.frame. Database with response, predictors, and partition values
response: character. Column name with species abundance.
predictors: character. Vector with the column names of quantitative predictor variables (i.e. continuous variables). Usage predictors = c("temp", "precipt", "sand")
predictors_f: character. Vector with the column names of qualitative predictor variables (i.e. ordinal or nominal variables type). Usage predictors_f = c("landform")
partition: character. Column name with training and validation partition groups.
predict_part: logical. Save predicted abundance for testing data. Default = FALSE
grid: tibble or data.frame. A dataframe with "n.trees", "interaction.depth", "n.minobsinnode" and "shrinkage" as columns and its values combinations as rows. If no grid is provided, function will create a default grid combining the next hyperparameters: nrounds = c(100, 200, 300), max_depth = c(4, 6, 8), eta = c(0.2, 0.4, 0.5), gamma = c(1, 5, 10), colsample_bytree = c(0.5, 1, 2), min_child_weight = c(0.5, 1, 2), subsample = c(0.5, 0.75, 1). In case one or more hyperparameters are provided, the function will complete the grid with the default values.
objective: character. The learning task and the corresponding learning objective. Default is "reg:squarederror", regression with squared loss.
metrics: character. Vector with one or more metrics from c("corr_spear","corr_pear","mae","pdisp","inter","slope").
n_cores: numeric. Number of cores used in parallel processing.
verbose: logical. If FALSE, disables all console messages. Default TRUE

Value

A list object with:

model: A "xgb.Booster" object from xgboost package. This object can be used to predicting.
predictors: A tibble with quantitative (c column names) and qualitative (f column names) variables use for modeling.
performance: A tibble with selected model's performance metrics calculated in adm_eval.
performance_part: A tibble with performance metrics for each test partition.
predicted_part: A tibble with predicted abundance for each test partition.
optimal_combination: A tibble with the selected hyperparameter combination and its performance.
all_combinations: A tibble with all hyperparameters combinations and its performance.

Examples

if (FALSE) {
require(dplyr)
# Database with species abundance and x and y coordinates
data("sppabund")
# Select data for a single species
some_sp <- sppabund %>%
  dplyr::filter(species == "Species two") %>%
  dplyr::select(-.part2, -.part3)
# Explore response variables
some_sp$ind_ha %>% range()
some_sp$ind_ha %>% hist()
# Here we balance number of absences
some_sp <-
  balance_dataset(some_sp, response = "ind_ha", absence_ratio = 0.2)
# Create a grid
xgb_grid <- expand.grid(
  nrounds = c(100, 300),
  max_depth = c(4, 6, 8),
  eta = c(0.2, 0.5),
  gamma = c(1, 5, 10),
  colsample_bytree = c(0.5, 1),
  min_child_weight = c(0.5, 1, 2),
  subsample = c(0.5, 1),
  stringsAsFactors = FALSE
)
# Tune a XGB model
tuned_xgb <- tune_abund_xgb(
  data = some_sp,
  response = "ind_ha",
  predictors = c("bio12", "elevation", "sand"),
  predictors_f = c("eco"),
  partition = ".part",
  predict_part = TRUE,
  metrics = c("corr_pear", "mae"),
  grid = xgb_grid,
  objective = "reg:squarederror",
  n_cores = 3
)
tuned_xgb
}