modelo: Species distribution modeling and cross validation

In mopa: Species Distribution MOdeling with Pseudo-Absences

Description

Species distribution modeling with k-fold cross validation. Algorithms supported are "glm", "svm", "maxent", "mars", "rf", "cart.rpart" and "cart.tree"

Usage

modelo(kdata, data, algorithm = c("glm", "svm", "maxent", "mars", "rf",
  "cart.rpart", "cart.tree"), algorithm.args = NULL, weighting = FALSE,
  threshold = NULL, tuneRF.args = NULL)

Arguments

kdata	Object returned by function leaveOneOut
data	Object returned by function biomat. 2D matrix with the dependent variable (presence/absence) in the first column and the independent variables in the rest (extracted from varstack)
algorithm	Any character of the following: "glm", "svm", "maxent", "mars", "rf", "cart.rpart" or "cart.tree"
algorithm.args	Further arguments to be passed to the selected algorithm for modeling (functions involved are described in details)
weighting	Logical for model fitting with weighted presence/absences-s. Applicable for algorithms "glm", "mars", "rf" and "cart.rpart". Default is FALSE.
threshold	Cut value between 0 and 1 to calculate the confusion matrix. Default is 0.5.
tuneRF.args	list of arguments from function tuneRF. Only used when algorihm = "rf"

Details

This function calculates the AUC with the function "auc" from package "PresenceAbsence". Package SDMTools must be detached.

Value

A list with six components:

model	fitted model using all data for training
auc	AUC statistic in the cross validation
kappa	kappa statistic in the cross validation
tss	true skill statistic in the cross validation
fold.models	fitted model with partitioned data
ObsPred	cross model prediction

Author(s)

M. Iturbide

mopa documentation built on May 2, 2019, 6:47 a.m.