# OptimalSet: AUCRF optimal set selection. In AUCRF: Variable Selection with Random Forest and the Area Under the Curve

## Description

Returns the optimal set of predictive variables selected by the AUC-RF method.

## Usage

 `1` ``` OptimalSet(object) ```

## Arguments

 `object` an object of class `AUCRF` as the result of `AUCRF` or `AUCRFcv` functions.

## Value

A data.frame with the selected variables ordered by the initial ranking, their importance values (initial ranking) and, if available, the probability of selection value measured by `AUCRFcv` function.

`AUCRF`, `AUCRFcv`.

## Examples

 ```1 2``` ``` data(fitCV) OptimalSet(fitCV) ```

### Example output

```Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
AUCRF 1.1
Name Importance Prob.Selection
1    SNP9  15.047305           1.00
2    SNP4  12.912120           1.00
3    SNP3  10.486599           1.00
4    SNP7   9.767075           1.00
5    SNP8   9.283819           1.00
6    SNP2   9.043039           1.00
7    SNP6   8.743129           1.00
8   SNP10   8.465736           1.00
9    SNP5   7.844703           1.00
10   SNP1   7.533021           1.00
11 SNP369   2.677609           0.35
12 SNP584   2.565316           0.19
13 SNP747   2.504847           0.09
14  SNP47   2.469360           0.26
15  SNP55   2.469196           0.14
16 SNP674   2.445041           0.24
17 SNP354   2.441501           0.04
18 SNP993   2.424503           0.16
19 SNP661   2.423057           0.51
20  SNP73   2.399690           0.03
21 SNP690   2.398267           0.56
22  SNP14   2.390978           0.05
23 SNP878   2.387848           0.50
24 SNP651   2.353301           0.00
25 SNP191   2.349521           0.36
26 SNP684   2.346010           0.16
27 SNP278   2.341461           0.06
28 SNP771   2.336632           0.04
29 SNP575   2.318485           0.71
30 SNP544   2.307716           0.61
31 SNP726   2.299561           0.13
32 SNP336   2.279044           0.07
```

AUCRF documentation built on May 29, 2017, 9:29 p.m.