Description Usage Arguments Value References See Also Examples

Given an object returned by `runBRT`

, extract `devBern`

, `rmse`

, `auc`

, `Kappa`

, `sensitivity`

and `specificity`

and proportion correctly classified (`pcc`

) validation statistics - calculated using either the `PresenceAbsence`

or `seegSDM`

functions. Note that `auc`

is calculated with a `seegSDM`

clone of the auc function in `PresenceAbsence`

but in which worse-than-random AUC scores are not inverted.

If `cv = TRUE`

the estimates returned are the means of the validation statistics and their standard deviations calculated against the witheld data for each of the `n.folds`

folds in the BRT run. That is, if the arguments `n.folds = 10, bag.fraction = 0.75`

are passed to `runBRT`

, the resulting BRT model will be an average of 10 separate BRT models, each of them train on a subset of 75% of the data. The validation statistics for each fold are calculated by comparing the predictions of each fold model against the 25% of the data which was witheld for that fold. Estimated standard deviations for these statistics are also calculated (by the functions in the `PresenceAbsence`

package). The mean of these statistics across the 10 folds is what is reported.

If `cv = TRUE`

*and* `pwd = TRUE`

, these cv statistics are calculated using the pairwise distance sampling procedure (`pwdSample`

) of Hijmans (2012) to ensure that accuracy statistics are not inflated by increasing the pseudo-absence selection distance in presence-only models

If `cv = FALSE`

the statistics are calculated once on the full training set using the final full model. `pwd = TRUE`

cannot be used in this case.

1 |

`object` |
A list of BRT model bootstraps, each element being an output from runBRT. |

`cv` |
Whether to calculate cross-validation statistics using folds ( |

`pwd` |
Whether to use the pairwise distance sampling procedure ( |

`threshold` |
The threshold distance for the pairwise distance sampling procedure, passed directly to the argument |

`...` |
Other arguments to pass to |

A vector giving the mean cross-validation statistics and mean standard deviations for these across the folds (see decription for details).

Hijmans, R.J., 2012. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null-model. Ecology 93: 679-688

`runBRT`

, `PresenceAbsence`

, `Kappa`

, `auc`

, `sensitivity`

, `specificity`

, `pcc`

, `pwdSample`

1 | ```
# TO DO
``` |

SEEG-Oxford/seegSDM documentation built on May 10, 2017, 10:25 a.m.

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