AUCRF {AUCRF} | R Documentation

## Variable Selection with Random Forest and the Area Under the Curve

### Description

AUCRF is an algorithm for variable selection using Random Forest based on optimizing the area-under-the ROC curve (AUC) of the Random Forest. The proposed strategy implements a backward elimination process based on the initial ranking of the variables.

### Usage

      AUCRF(formula, data, k0 = 1, pdel = 0.2, ranking=c("MDG","MDA"), ...)

### Arguments

`formula` | an object of class `formula`: a symbolic description of the model to be fitted. The details of model specification are given in `Details`.
`data` | a data frame containing the variables in the model. Dependent variable must be a binary variable defined as `factor` and codified as `1` for positives (e.g. cases) and `0` for negatives (e.g. controls).
`k0` | number of remaining variables for stopping the backward elimination process. By default `k0=1`.
`pdel` | fraction of remaining variables to be removed in each step. By default `pdel=0.2`. If `pdel=0`, only one variable is removed each time.
`ranking` | specifies the importance measure provided by `randomForest` for ranking the variables. There are two options MDG (by default) for MeanDecreaseGini and MDA for MeanDecreaseAccuracy.
`...` | optional parameters to be passed to the `randomForest` function. If no arguments are specified, default arguments of `randomForest` function will be used.

### Details

The AUC-RF algorithm is described in detail in Calle et. al.(2011). The following is a summary:

Ranking and AUC of the initial set:
Perform a random forest using all predictor variables and the response, as specified in the `formula` argument, and compute the AUC of the random forest. Based on the selected measure of importance (by default MDG), obtain a ranking of predictors.

Elimination process:
Based on the variables ranking, remove the less important variables (fraction of variables specified in `pdel` argument). Perform a new random forest with the remaining variables and compute its AUC. This step is iterated until the number of remaining variables is less or equal than `k0`.

Optimal set:
The optimal set of predictive variables is considered the one giving rise to the Random Forest with the highest OOB-AUCopt. The number of selected predictors is denoted by Kopt

### Value

An object of class `AUCRF`, which is a list with the following components:

`call` | the original call to `AUCRF`.
`data` | the `data` argument.
`ranking` | the ranking of predictors based on the importance measure.
`Xopt` | optimal set of predictors obtained.
`OOB-AUCopt` | AUC obtained for the optimal set of predictors.
`Kopt` | size of the optimal set of predictors obtained.
`AUCcurve` | values of AUC obtained for each set of predictors evaluated in the elimination process.
`RFopt` | the `randomForest` adjusted with the optimal set.

### References

Calle ML, Urrea V, Boulesteix A-L, Malats N (2011) "AUC-RF: A new strategy for genomic profiling with Random Forest". Human Heredity. (In press)

### See Also

`OptimalSet`, `AUCRFcv`, `randomForest`.

### Examples

      # load the included example dataset. This is a simulated case/control study
      # data set with 4000 patients (2000 cases / 2000 controls) and 1000 SNPs,
      # where the  first 10 SNPs have a direct association with the outcome:
      data(exampleData)

      # call AUCRF process: (it may take some time)
      # fit <- AUCRF(Y~., data=exampleData)

      # The result of this example is included for illustration purpose:

      data(fit)
      summary(fit)
      plot(fit)

      # Additional randomForest parameters can be included, otherwise default
      # parameters of randomForest function will be used:
      # fit <- AUCRF(Y~., data=exampleData, ntree=1000, nodesize=20)

[Package AUCRF version 1.1 Index]