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Calculate permutation-based variable importance scores for SDMs

Source: R/sdm_varimp.R
sdm_varimp.Rd

This function calculates variable importance scores for species distribution models (SDMs) based on permutation-based approach.

Usage

sdm_varimp(
  models,
  data,
  response,
  predictors,
  n_sim = 50,
  n_cores = 1,
  thr = NULL,
  clamp = TRUE,
  pred_type = "cloglog"
)

Arguments

models

list of one or more models fitted with fit_ or tune_ functions. In case use models fitted with fit_ensemble or esm_ family function only one model could be used. Usage models = mglm or models = list(mglm, mraf, mgbm)

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 predictor variables. Usage predictors = c("aet", "cwd", "tmin")

n_sim

integer. The number of Monte Carlo replications to perform. Default is 50. The results from each replication are averaged together (the standard deviation will also be returned).

n_cores

numeric. Number of cores use for parallelization. Default 1

thr

character. Threshold criterion used to get binary suitability values (i.e. 0,1). Used for threshold-dependent performance metrics. It is possible to use more than one threshold type. A vector must be provided 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 the highest.

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

#' If more than one threshold type is used, concatenate threshold types, 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 type is specified

clamp

logical. If TRUE, predictors and features are restricted to the range seen during model training.

pred_type

character. Type of response required available "link", "exponential", "cloglog" and "logistic". Default "cloglog"

Value

a tibble with the columns:

  • model: model name

  • threshold: threshold names

  • predictors: predictor names

  • from TPR to IMAE: performance metrics

Details

This function calculates variable importance scores for species distribution models (SDMs) based on permutation-based approach. Thus, the function calculates the model performance using the original data and the permuted data. The difference between the two performances is the variable importance score.

Examples

if (FALSE) { # \dontrun{
require(tidyr)
require(dplyr)

data(abies)
abies

data(backg)
backg

# In this example we will partition the data using the k-fold method

abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 5)
)

backg2 <- part_random(
  data = backg,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 5)
)

max_t1 <- fit_max(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
  predictors_f = c("landform"),
  partition = ".part",
  background = backg2,
  thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
  clamp = TRUE,
  classes = "default",
  pred_type = "cloglog",
  regmult = 1
)

net_t1 <- fit_net(
  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")
)

svm_f1 <- 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")
)

vip_t <- sdm_varimp(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "ppt_jja", "pH", "awc", "depth", "landform"),
  models = list(max_t1, net_t1, svm_f1),
  clamp = TRUE,
  pred_type = "cloglog",
  thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
  n_sim = 50,
  n_cores = 5
)

vip_t

# Plot the variable importance for AUC TSS and SORENSEN for the
# threshold that maximizes Sorensen metric and Maxent
vip_t %>%
  pivot_longer(
    cols = TPR:IMAE,
    names_to = "metric",
    values_to = "value"
  ) %>%
  dplyr::filter(threshold == "max_sorensen") %>%
  dplyr::filter(metric %in% c("AUC", "TSS", "SORENSEN")) %>%
  dplyr::filter(model == "max") %>%
  ggplot(aes(x = reorder(predictors, value), y = value, fill = predictors)) +
  geom_col(
    col = "black",
    show.legend = FALSE
  ) +
  facet_wrap(~metric, scales = "free_x") +
  labs(x = "Predictors", y = "Variable Importance") +
  theme_classic() +
  coord_flip()
} # }