sdm_varimp: Calculate permutation-based variable importance scores for...

In sjevelazco/flexsdm: Tools for Data Preparation, Fitting, Prediction, Evaluation, and Post-Processing of Species Distribution Models

Calculate permutation-based variable importance scores for SDMs

Description

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"

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.

Value

a tibble with the columns:

• model: model name

• threshold: threshold names

• predictors: predictor names

• from TPR to IMAE: performance metrics

Examples

## Not run: 
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()

## End(Not run)

sjevelazco/flexsdm documentation built on Feb. 28, 2025, 9:07 a.m.