jackknife: Jackkniffe test for variable contribution

In enmpa: Ecological Niche Modeling using Presence-Absence Data

Jackkniffe test for variable contribution

Description

The Jackknife function providing a detailed reflection of the impact of each variable on the overall model, considering four difference measures: ROC-AUC, TSS, AICc, and Deviance.

Usage

jackknife(data, dependent, independent, user_formula = NULL, cv = 3,
          response_type = "l", weights = NULL)

Arguments

data	data.frame or matrix of data to be used in model calibration. Columns represent dependent and independent variables.
dependent	(character) name of dependent variable.
independent	(character) vector of name(s) of independent variable(s).
user_formula	(character) custom formula to test. Default = NULL.
cv	(numeric) number of folds to use for k-fold cross-validation exercises. Default = 3.
response_type	(character) a character string that must contain "l", "p", "q" or a combination of them. l = lineal, q = quadratic, p = interaction between two variables. Default = "l".
weights	(numeric) a vector with the weights for observations.

Value

list including model performance metrics (ROC-AUC, TSS, AICc, and deviance) for the complete model, model performance when excluding a specific predictor, and the independent contribution of that predictor to the model.

Examples

# Load data
data("enm_data", package = "enmpa")

jk <- jackknife(data = enm_data,
                dependent = "Sp",
                independent = c("bio_1", "bio_12"),
                user_formula = NULL,
                cv = 3, response_type = "lpq")
jk

# plot JK's results
plot_jk(jk, metric = "TSS")
plot_jk(jk, metric = "ROC_AUC")
plot_jk(jk, metric = "AIC")
plot_jk(jk, metric = "Residual_deviance")

enmpa documentation built on June 13, 2025, 1:10 a.m.