This function will return a variable importance value for each variable involved within your model.

1 | ```
variables_importance(model, data, method="full_rand", nb_rand=1, ...)
``` |

`model` |
the model you want to study variables importance (one of the models supported within biomod2, ensemble models are also supported) |

`data` |
the |

`method` |
the randomisation method (only 'full_rand' available yet) |

`nb_rand` |
the number of permutation done for each variable |

`...` |
additional args (not implemented yet) |

It's more or less base on the same principle than `randomForest`

variables importance algorithm. The principle is to shuffle a single variable of the given data. Make model prediction with this 'shuffled' data.set. Then we compute a simple correlation (Pearson's by default) between references predictions and the 'shuffled' one. The return score is 1-cor(pred_ref,pred_shuffled). The highest the value, the more influence the variable has on the model. A value of this 0 assumes no influence of that variable on the model. Note that this technique does not account for interactions between the variables.

a `list`

of class "BIOMOD_variables_importances" which contains:

mata

`data.frame`

containing variables importance scores for each permutation run.

Damien Georges

`randomise_data`

, `full_suffling`

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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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