This is the extractor function for variable importance measures as
produced by `wsrf`

.

1 2 |

`x` |
an object of class |

`type` |
either 1 or 2, specifying the type of importance measure (1=mean decrease in accuracy, 2=mean decrease in node impurity). |

`class` |
for classification problem, which class-specific measure to return. |

`scale` |
for permutation based measures, should the measures be divided their “standard errors”? |

`...` |
not used. |

Here are the definitions of the variable importance measures. The first measure is computed from permuting OOB data: For each tree, the prediction error on the out-of-bag portion of the data is recorded. Then the same is done after permuting each predictor variable. The difference between the two are then averaged over all trees, and normalized by the standard deviation of the differences.

The second measure is the total decrease in node impurities from splitting on the variable, averaged over all trees. The node impurity is measured by the Information Gain Ratio index.

A matrix of importance measure, one row for each predictor variable. The column(s) are different importance measures.

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