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Fit and validate Generalized Boosted Regression models based on Ensembles of Small Models approach

Source: R/esm_gbm.R
esm_gbm.Rd

This function constructs Generalized Boosted Regression using the Ensembles of Small Models (ESM) approach (Breiner et al., 2015, 2018).

Usage

esm_gbm(
  data,
  response,
  predictors,
  partition,
  thr = NULL,
  n_trees = 100,
  n_minobsinnode = NULL,
  shrinkage = 0.1
)

Arguments

data

data.frame. Database with the response (0,1) and predictors values.

response

character. Column name with species absence-presence data (0,1)

predictors

character. Vector with the column names of quantitative predictor variables (i.e. continuous variables). This can only construct models with continuous variables and does not allow categorical variables. Usage predictors = c("aet", "cwd", "tmin")

partition

character. Column name with training and validation partition groups.

thr

character. Threshold used to get binary suitability values (i.e. 0,1). It is useful for threshold-dependent performance metrics. It is possible to use more than one threshold type. It is necessary to provide a vector for this argument. The following threshold criteria are available:

  • equal_sens_spec: Threshold at which the sensitivity and specificity are equal.

  • max_sens_spec: Threshold at which the sum of the sensitivity and specificity is the highest (aka threshold that maximizes the TSS).

  • max_jaccard: The threshold at which the Jaccard index is the highest.

  • max_sorensen: The threshold at which the Sorensen index is highest.

  • max_fpb: The threshold at which FPB (F-measure on presence-background data) is highest.

  • sensitivity: Threshold based on a specified sensitivity value. Usage thr = c('sensitivity', sens='0.6') or thr = c('sensitivity'). 'sens' refers to sensitivity value. If a sensitivity value is not specified, the default value is 0.9.

In the case of use more than one threshold type it is necessary concatenate threshold types, e.g., thr=c('max_sens_spec', 'max_jaccard'), or thr=c('max_sens_spec', 'sensitivity', sens='0.8'), or thr=c('max_sens_spec', 'sensitivity'). Function will use all thresholds if no threshold is specified.

n_trees

Integer specifying the total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion. Default is 100.

n_minobsinnode

Integer specifying the minimum number of observations in the terminal nodes of the trees. Note that this is the actual number of observations, not the total weight. If n_minobsinnode is NULL, this parameter will assume a value equal to nrow(data)*0.5/4. Default is NULL.

shrinkage

Numeric. This parameter applied to each tree in the expansion. Also known as the learning rate or step-size reduction; 0.001 to 0.1 usually works, but a smaller learning rate typically requires more trees. Default is 0.1.

Value

A list object with:

  • esm_model: A list with "gbm" class object from gbm package for each bivariate model. This object can be used for predicting ensembles of small models with sdm_predict function.

  • predictors: A tibble with variables use for modeling.

  • performance: Performance metrics (see sdm_eval). Threshold dependent metrics are calculated based on the threshold specified in thr argument.

Details

This method consists of creating bivariate models with all the pair-wise combinations of predictors and perform an ensemble based on the average of suitability weighted by Somers' D metric (D = 2 x (AUC -0.5)). ESM is recommended for modeling species with few occurrences. This function does not allow categorical variables because the use of these types of variables could be problematic when using with few occurrences. For further detail see Breiner et al. (2015, 2018).

References

  • Breiner, F. T., Guisan, A., Bergamini, A., & Nobis, M. P. (2015). Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution, 6(10), 1210-218. https://doi.org/10.1111/2041-210X.12403

  • Breiner, F. T., Nobis, M. P., Bergamini, A., & Guisan, A. (2018). Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods in Ecology and Evolution, 9(4), 802-808. https://doi.org/10.1111/2041-210X.12957

See also

esm_gam, esm_gau, esm_glm, esm_max, esm_net, and esm_svm.

Examples

if (FALSE) {
data("abies")
require(dplyr)

# Using k-fold partition method
set.seed(10)
abies2 <- abies %>%
  na.omit() %>%
  group_by(pr_ab) %>%
  dplyr::slice_sample(n = 10) %>%
  group_by()

abies2 <- part_random(
  data = abies2,
  pr_ab = "pr_ab",
  method = c(method = "rep_kfold", folds = 3, replicates = 5)
)
abies2

# Without threshold specification and with kfold
esm_gbm_t1 <- esm_gbm(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "cwd", "tmin", "ppt_djf", "ppt_jja", "pH", "awc", "depth"),
  partition = ".part",
  thr = NULL,
  n_trees = 100,
  n_minobsinnode = NULL,
  shrinkage = 0.1
)

esm_gbm_t1$esm_model # bivariate model
esm_gbm_t1$predictors
esm_gbm_t1$performance
}