multModEv | R Documentation |

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.

```
multModEv(models = NULL, obs.data = NULL, pred.data = NULL,
measures = modEvAmethods("multModEv"), standardize = FALSE,
thresh = NULL, bin.method = NULL, verbosity = 0, ...)
```

`models` |
a list of model object(s) of class "glm", all applied to the same data set. Evaluation is based on the cases included in the models. |

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

`pred.data` |
a data frame with the corresponding predicted (training or test) values, with both rows and columns in the same order as in 'obs.data'. This argument is ignored if 'models' is provided. Note that, for calibration measures (based on |

`measures` |
character vector of the evaluation measures to calculate. The default is all implemented measures, which you can check by typing 'modEvAmethods("multModEv")'. But beware: calibration measures (i.e., HL and Miller) are only valid if your predicted values reflect actual presence probability (not favourability, habitat suitability or others); you should exclude them otherwise. |

`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

`threshMeasures`

```
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", "AUCPR", "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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