# varImpAUC

##### varImpAUC

Computes the variable importance regarding the AUC. Bindings are not taken into account in the AUC definition as they did not provide as good results as the version without bindings in the paper of Janitza et. al (2013) (see References section).

##### Usage

```
varImpAUC(object, mincriterion = 0, conditional = FALSE,
threshold = 0.2, nperm = 1, OOB = TRUE, pre1.0_0 = conditional)
```

##### Arguments

- object
An object as returned by cforest.

- mincriterion
The value of the test statistic or 1 - p-value that must be exceeded in order to include a split in the computation of the importance. The default mincriterion = 0 guarantees that all splits are included.

- conditional
The value of the test statistic or 1 - p-value that must be exceeded in order to include a split in the computation of the importance. The default mincriterion = 0 guarantees that all splits are included.

- threshold
The threshold value for (1 - p-value) of the association between the variable of interest and a covariate, which must be exceeded inorder to include the covariate in the conditioning scheme for the variable of interest (only relevant if conditional = TRUE). A threshold value of zero includes all covariates.

- nperm
The number of permutations performed.

- OOB
A logical determining whether the importance is computed from the out-of-bag sample or the learning sample (not suggested).

- pre1.0_0
Prior to party version 1.0-0, the actual data values were permuted according to the original permutation importance suggested by Breiman (2001). Now the assignments to child nodes of splits in the variable of interest are permuted as described by Hapfelmeier et al. (2012), which allows for missing values in the explanatory variables and is more efficient wrt memory consumption and computing time. This method does not apply to conditional variable importances.

##### Details

For using the original AUC definition and multiclass AUC you can use the varImp function and specify the particular measure.

##### Value

Vector with computed permutation importance for each variable

##### References

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-119

##### Examples

```
# NOT RUN {
# multiclass case
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = cforest(Species ~ ., data = iris2,control = cforest_unbiased(mtry = 2, ntree = 50))
set.seed(123)
varImpAUC(object = iris.cf)
# }
```

*Documentation reproduced from package varImp, version 0.3, License: GPL-3*