Description Usage Arguments Value Author(s) See Also Examples

If you have a list of GLM model objects (created, e.g., with the `multGLM`

function of the 'fuzzySim' R-Forge package), or a data frame with presence-absence data and the corresponding predicted values for a set of species, you can use the `multModEv`

function to get a set of evaluation measures for all models simultaneously, as long as they all have the same sample size.

1 2 3 |

`models` |
a |

`obs.data` |
a data frame with observed (training or test) binary data. This argument is ignored if |

`pred.data` |
a data frame with the corresponding predicted (training or test) values, with both rows and columns in the same order as in |

`measures` |
character vector of the evaluation measures to calculate. The default is all implemented measures, which you can check by typing |

`standardize` |
logical, whether to standardize measures that vary between -1 and 1 to the 0-1 scale (see |

`thresh` |
argument to pass to |

`bin.method` |
the method with which to divide the data into groups or bins, for calibration or reliability measures such as |

`verbosity` |
integer specifying the amount of messages or warnings to display. Defaults to 0, but can also be 1 or 2 for more messages from the functions within. |

`...` |
optional arguments to pass to |

A data frame with the value of each evaluation measure for each model.

A. Marcia Barbosa

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
data(rotif.mods)
eval1 <- multModEv(models = rotif.mods$models[1:6], thresh = 0.5,
bin.method = "n.bins", fixed.bin.size = TRUE)
head(eval1)
eval2 <- multModEv(models = rotif.mods$models[1:6], thresh = "preval",
measures = c("AUC", "CCR", "Sensitivity", "TSS"))
head(eval2)
# you can also calculate evaluation measures for a set of
# observed vs predicted data, rather than from model objects:
obses <- sapply(rotif.mods$models, `[[`, "y")
preds <- sapply(rotif.mods$models, `[[`, "fitted.values")
eval3 <- multModEv(obs.data = obses[ , 1:4], pred.data = preds[ , 1:4],
thresh = "preval", bin.method = "prob.bins")
head(eval3)
``` |

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