Description Usage Arguments Value Author(s) References See Also

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"

1 2 3 4 |

`y` |
RasterStack of variables for modelling |

`x` |
Object returned by function |

`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 |

`tuneRF.args` |
list of arguments from function |

`plotnames` |
names to be printed in the diagrams |

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 |

M. Iturbide

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.

`mopaPredict`

, `pseudoAbsences`

, `backgroundGrid`

,
`OCSVMprofiling`

, `backgroundRadius`

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