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Fit and validate Support Vector Machine models based on Ensembles of Small of Models approach

Source: R/esm_svm.R
esm_svm.Rd

This function constructs Support Vector Machine models using the Ensembles of Small Models (ESM) approach (Breiner et al., 2015, 2018).

Usage

esm_svm(
  data,
  response,
  predictors,
  partition,
  thr = NULL,
  sigma = "automatic",
  C = 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 function only can 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 is 0.9

If the user wants to include 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

sigma

numeric. Inverse kernel width for the Radial Basis kernel function "rbfdot". Default "automatic".

C

numeric. Cost of constraints violation, the 'C' constant of the regularization term in the Lagrange formulation. Default 1

Value

A list object with:

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

  • predictors: A tibble with variables use for modeling.

  • performance: Performance metric (see sdm_eval). Those threshold dependent metric 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. Further detail see Breiner et al. (2015, 2018). This function constructs 'C-svc' classification type and uses Radial Basis kernel "Gaussian" function (rbfdot). See details in ksvm

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_gbm, esm_glm, esm_max,, and esm_net.

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_svm_t1 <- esm_svm(
  data = abies2,
  response = "pr_ab",
  predictors = c(
    "aet", "cwd", "tmin", "ppt_djf", "ppt_jja",
    "pH", "awc", "depth"
  ),
  partition = ".part",
  thr = NULL
)

esm_svm_t1$esm_model # bivariate model
esm_svm_t1$predictors
esm_svm_t1$performance
}