AUCRFcv: Repeated cross validation of the AUC-RF process.
In AUCRF: Variable Selection with Random Forest and the Area Under the Curve

Description Usage Arguments Details Value References See Also Examples

Performes a repeated cross validation analysis and computes the probability of selection for each variable.

1	AUCRFcv(x, nCV = 5, M = 20)

`x`	an object of class `AUCRF`.
`nCV`	number of folds in cross validation. By default a 5-fold cross validation is performed.
`M`	number of cross validation repetitions.

The results of this repeated cross validation analysis are (1) a corrected estimation of the predictive accuracy of the selected variables and (2) an estimate of the probability of selection for each variable.
The AUC-RF algorithm is exhaustively described in Calle et. al.(2011).

The same AUCRF object passed (see AUCRF) as argument but updated with the following components:

`cvAUC`	mean of AUC values in test datasets of the optimal sets of predictors.
`Psel`	probability of selection of each variable as the proportion of times that is selected by AUC-RF method.

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)

OptimalSet, AUCRF, randomForest.

  # Next steps take some time
  
  # load included AUCRF result example:
  # data(fit)
  
  # call AUCRFcv process:
  # fitCV <- AUCRFcv(fit)
  
  # The result of this example is included:
  
  data(fitCV)
  summary(fitCV)
  plot(fitCV)

Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
AUCRF 1.1

Number of selected variables: Kopt= 32 
AUC of selected variables: OOB-AUCopt= 0.7787711 
AUC from cross validation: 0.759109 
Importance Measure: MDG 

   Selected.Variables Importance Prob.Select
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