mopaTrain0: Easy species distribution modeling and cross validation

In mopa: Species Distribution MOdeling with Pseudo-Absences

Description

Species distribution modeling and k-fold cross validation for a set of presence/absence data per species, also considering different background extents (optional). Algorithms supported are "glm", "svm", "maxent", "mars", "rf", "cart.rpart" and "cart.tree"

Usage

mopaTrain0(y, x, k = 10, algorithm = c("glm", "svm", "maxent", "mars", "rf",
  "cart.rpart", "cart.tree"), algorithm.args = NULL, weighting = FALSE,
  threshold = NULL, diagrams = FALSE, tuneRF.args = NULL,
  plotnames = "unnamed")

Arguments

y	RasterStack of variables for modelling
x	Object returned by function link[mopa]{pseudoAbsences} or list/s of data frames with coordinates in the first two columns and presence/absence (1=presence, 0=absence) in the third column.
k	Integer. Number of folds for cross validation. Default is 10
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 "glm", "mars" and "rf" fitting with weighted presence/absences-s. Default is FALSE.
threshold	Cut value between 0 and 1 to calculate the confusion matrix. Default is NULL (see Details).
diagrams	logical. Only applied if x contains data for different background extents (see backgroundRadius and pseudoAbsences). Should diagrams of AUC extent fitting be printed? default is FALSE.
tuneRF.args	list of arguments from function tuneRF. Only used when algorihm = "rf"
plotnames	names to be printed in the diagrams

Value

A list of six components is returned for each species in x:

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

References

Iturbide, M., Bedia, J., Herrera, S., del Hierro, O., Pinto, M., Gutierrez, J.M., 2015. A framework for species distribution modelling with improved pseudo-absence generation. Ecological Modelling. DOI:10.1016/j.ecolmodel.2015.05.018.

See Also

mopaPredict, pseudoAbsences, backgroundGrid, OCSVMprofiling, backgroundRadius

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