proc_or_aicc: Partial ROC, omission rates, and AICc calculations in concert...

In manubio13/ku.enm: Detailed development of ecological niche models using Maxent

Partial ROC, omission rates, and AICc calculations in concert (helper)

Description

proc_or_aicc performs a series of step by step processes that help to read files from directores, extract necessary data, and evaluate Maxent predictions based on partial ROC, omission rates, and AICc values.

Usage

proc_or_aicc(occ.joint, occ.tra, occ.test, raw.folders, log.folders,
             threshold = 5, rand.percent = 50, iterations = 500, kept = TRUE)

Arguments

occ.joint	(character) the name of csv file with training and testing occurrences combined; columns must be: species, longitude, and latitude.
occ.tra	(character) the name of the csv file with the training occurrences; columns as in occ.joint.
occ.test	(character) the name of the csv file with the evaluation occurrences; columns as in occ.joint.
raw.folders	(character) vector of names of directories containing models created with all occurrences and raw outputs.
log.folders	(character) vector of names of directories containing models created with training occurrences and logistic outputs.
threshold	(numeric) the percentage of training data omission error allowed (E); default = 5.
rand.percent	(numeric) the percentage of data to be used for the bootstraping process when calculating partial ROCs; default = 50.
iterations	(numeric) the number of times that the bootstrap is going to be repeated; default = 500.
kept	(logical) if FALSE, all candidate models will be erased after evaluation, default = TRUE.

Value

A data.frame with the results of partial ROC, omission rates, and AICc metrics for all candidate models.

manubio13/ku.enm documentation built on Jan. 5, 2024, 5:55 a.m.