evaluate_po: Evaluate the model based on presence-only data.

In itsdm: Isolation Forest-Based Presence-Only Species Distribution Modeling

Evaluate the model based on presence-only data.

Description

This function will calculate two major types of evaluation metrics in terms of presence-only data. The first type is presence-only customized metrics, such as Contrast Validation Index (CVI), continuous Boyce index (CBI), and ROC_ratio. The second type is presence-background evaluation metrics by extracting background points as pseudo absence observations.

Usage

evaluate_po(
  model,
  occ_pred,
  bg_pred = NULL,
  var_pred,
  threshold = NULL,
  visualize = FALSE
)

Arguments

model	(isolation_forest) The extended isolation forest SDM. It could be the item model of POIsotree made by function isotree_po.
occ_pred	(vector of numeric) A vector contains predicted values at occurrence locations.
bg_pred	(vector of numeric) the vector contains predicted values with same number of background points.
var_pred	(vector of numeric) the vector contains predicted values of the whole area. The reason to take a vector is to keep this function flexible for multiple types of output.
threshold	(numeric or NULL) The threshold to calculate threshold-based evaluation metrics. If NULL, a recommended threshold will be calculated based on optimal TSS value. The default is NULL.
visualize	(logical) If TRUE, plot the evaluation figures. The default is FALSE.

Details

• CVI is the proportion of presence points falling in cells having a threshold (0.5 for example) habitat suitability index minus the proportion of cells within this range of threshold of the model. Here we used varied thresholds: 0.25, 0.5, and 0.75.

• continuous Boyce index (CBI) is made with a 100 resolution of moving windows and Kendall method.

• ROC_ratio curve plots the proportion of presences falling above a range of thresholds against the proportion of cells falling above the range of thresholds. The area under the modified ROC curve was then called AUC_ratio.

• Sensitivity (TPR) = TP/(TP + FN)

• Specificity (TNR) = TN/(TN + FP)

• True skill statistic (TSS) = Sensitivity + specificity - 1

• Jaccard's similarity index = TP/(FN + TP + FP)

• Sørensen's similarity index (F-measure) = 2TP/(FN + 2TP + FP)

• Overprediction rate = FP/(TP + FP)

• Underprediction rate = FN/(TP + FN)

Value

(POEvaluation) A list of

• po_evaluation is presence-only evaluation metrics. It is a list of

  • cvi (list) A list of CVI with 0.25, 0.5, and 0.75 as threshold

  • boyce (list) A list of items related to continuous Boyce index (CBI)

  • roc_ratio (list) A list of ROC ratio and AUC ratio

• pb_evaluation is presence-background evaluation metrics. It is a list of

  • confusion matrix (table) A table of confusion matrix. The columns are true values, and the rows are predicted values.

  • sensitivity (numeric) The sensitivity or TPR

  • specificity (numeric) The specificity or TNR

  • TSS (list) A list of info related to true skill statistic (TSS)

    • cutoff (vector of numeric) A vector of cutoff threshold values

    • tss (vector of numeric) A vector of TSS for each cutoff threshold

    • Recommended threshold (numeric) A recommended threshold according to TSS

    • Optimal TSS (numeric) The best TSS value

  • roc (list) A list of ROC values and AUC value

  • Jaccard's similarity index (numeric) The Jaccard's similarity index

  • Sørensen's similarity index (numeric) The Sørensen's similarity index or F-measure

  • Overprediction rate (numeric) The Overprediction rate

  • Underprediction rate (numeric) The Underprediction rate

References

• Peterson, A. Townsend, Monica Papeş, and Jorge Soberón. "Rethinking receiver operating characteristic analysis applications in ecological niche modeling." Ecological modelling 213.1 (2008): 63-72. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ecolmodel.2007.11.008")}

• Hirzel, Alexandre H., et al. "Evaluating the ability of habitat suitability models to predict species presences." Ecological modelling 199.2 (2006): 142-152. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ecolmodel.2006.05.017")}

• Hirzel, Alexandre H., and Raphaël Arlettaz. "Modeling habitat suitability for complex species distributions by environmental-distance geometric mean." Environmental management 32.5 (2003): 614-623. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00267-003-0040-3")}

• Leroy, Boris, et al. "Without quality presence-absence data, discrimination metrics such as TSS can be misleading measures of model performance." Journal of Biogeography 45.9 (2018): 1994-2002. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/jbi.13402")}

See Also

print.POEvaluation, plot.POEvaluation

Examples

# Using a pseudo presence-only occurrence dataset of
# virtual species provided in this package
library(dplyr)
library(sf)
library(stars)
library(itsdm)

data("occ_virtual_species")
obs_df <- occ_virtual_species %>% filter(usage == "train")
eval_df <- occ_virtual_species %>% filter(usage == "eval")
x_col <- "x"
y_col <- "y"
obs_col <- "observation"

# Format the observations
obs_train_eval <- format_observation(
  obs_df = obs_df, eval_df = eval_df,
  x_col = x_col, y_col = y_col, obs_col = obs_col,
  obs_type = "presence_only")

env_vars <- system.file(
  'extdata/bioclim_tanzania_10min.tif',
  package = 'itsdm') %>% read_stars() %>%
  slice('band', c(1, 5, 12, 16))

# With perfect_presence mode,
# which should be very rare in reality.
mod <- isotree_po(
  obs_mode = "perfect_presence",
  obs = obs_train_eval$obs,
  obs_ind_eval = obs_train_eval$eval,
  variables = env_vars, ntrees = 10,
  sample_size = 0.8, ndim = 2L,
  seed = 123L, nthreads = 1,
  response = FALSE,
  spatial_response = FALSE,
  check_variable = FALSE)

# Without background samples or absences
eval_train <- evaluate_po(
  mod$model,
  occ_pred = mod$pred_train$prediction,
  var_pred = na.omit(as.vector(mod$prediction[[1]])))
print(eval_train)

# With background samples
bg_pred <- st_extract(
  mod$prediction, mod$background_samples) %>%
  st_drop_geometry()
eval_train <- evaluate_po(
  mod$model,
  occ_pred = mod$pred_train$prediction,
  bg_pred = bg_pred$prediction,
  var_pred = na.omit(as.vector(mod$prediction[[1]])))
plot(eval_train)
#'

itsdm documentation built on July 9, 2023, 6:45 p.m.