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Fit and validate Generalized Boosted Regression models

Source: R/fit_abund_gbm.R
fit_abund_gbm.Rd

Fit and validate Generalized Boosted Regression models

Usage

fit_abund_gbm(
  data,
  response,
  predictors,
  predictors_f = NULL,
  fit_formula = NULL,
  partition,
  predict_part = FALSE,
  distribution,
  n.trees = 100,
  interaction.depth = 5,
  n.minobsinnode = 5,
  shrinkage = 0.1,
  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")

fit_formula

formula. A formula object with response and predictor variables (e.g. formula(abund ~ temp + precipt + sand + landform)). Note that the variables used here must be consistent with those used in response, predictors, and predictors_f arguments. Default NULL

partition

character. Column name with training and validation partition groups.

predict_part

logical. Save predicted abundance for testing data. Default = FALSE

distribution

character. A string specifying the distribution to be used. See gbm::gbm documentation for details.

n.trees

integer. The total number of trees to fit.

interaction.depth

integer. The maximum depth of each tree. Default 5

n.minobsinnode

integer. The minimum number of observations in the terminal nodes of the trees. Default 5

shrinkage

numeric. The learning rate of the algorithm. Default 0.1

verbose

logical. If FALSE, disables all console messages. Default TRUE

Value

A list object with:

  • model: A "gbm" class object from gbm package. This object can be used for predicting.

  • predictors: A tibble with quantitative (c column names) and qualitative (f column names) variables use for modeling.

  • performance: Averaged performance metrics (see adm_eval).

  • performance_part: Performance metrics for each replica and partition.

  • predicted_part: Observed and predicted abundance for each test partition.

Examples

if (FALSE) {
require(terra)
require(dplyr)

# Database with species abundance and x and y coordinates
data("sppabund")

# Extract data for a single species
some_sp <- sppabund %>%
  dplyr::filter(species == "Species one") %>%
  dplyr::select(-.part2, -.part3)

# Explore reponse 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)

# Fit a GBM model
mgbm <- fit_abund_gbm(
  data = some_sp,
  response = "ind_ha",
  predictors = c("bio12", "elevation", "sand"),
  predictors_f = c("eco"),
  partition = ".part",
  distribution = "gaussian",
  n.trees = 100,
  interaction.depth = 5,
  n.minobsinnode = 5,
  shrinkage = 0.1,
  predict_part = TRUE
)

mgbm
}