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Fit and validate Support Vector Machine models

Source: R/fit_svm.R
fit_svm.Rd

Fit and validate Support Vector Machine models

Usage

fit_svm(
  data,
  response,
  predictors,
  predictors_f = NULL,
  fit_formula = NULL,
  partition,
  thr = NULL,
  sigma = "automatic",
  C = 1
)

Arguments

data

data.frame. Database with 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). Usage predictors = c("aet", "cwd", "tmin")

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(pr_ab ~ aet + ppt_jja + pH + awc + depth + landform)). Note that the variables used here must be consistent with those used in response, predictors, and predictors_f arguments

partition

character. Column name with training and validation partition groups.

thr

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

  • lpt: The highest threshold at which there is no omission.

  • 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 used is 0.9

If more than one threshold type is used they must be concatenated, e.g., thr=c('lpt', 'max_sens_spec', 'max_jaccard'), or thr=c('lpt', 'max_sens_spec', 'sensitivity', sens='0.8'), or thr=c('lpt', '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:

  • model: A "ksvm" class object from kernlab 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: Performance metric (see sdm_eval). Threshold dependent metrics are calculated based on the threshold specified in the argument.

  • data_ens: Predicted suitability for each test partition based on the best model. This database is used in fit_ensemble

Details

This function constructs 'C-svc' classification type and uses Radial Basis kernel "Gaussian" function (rbfdot). See details details in ksvm.

See also

fit_gam, fit_gau, fit_gbm, fit_glm, fit_max, fit_net, and fit_raf.

Examples

if (FALSE) { # \dontrun{
data("abies")

# Using k-fold partition method
abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 5)
)
abies2

svm_t1 <- fit_svm(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
  predictors_f = c("landform"),
  partition = ".part",
  thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
  fit_formula = NULL
)

names(svm_t1)
svm_t1$model
svm_t1$predictors
svm_t1$performance
svm_t1$data_ens

# Using bootstrap partition method and only with presence-absence
# and get performance for several method
abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "boot", replicates = 10, proportion = 0.7)
)
abies2

svm_t2 <- fit_svm(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
  predictors_f = c("landform"),
  partition = ".part",
  thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
  fit_formula = NULL
)
svm_t2
} # }