.cv_binomialRF {binomialRF} | R Documentation |

## random forest feature selection based on binomial exact test

### Description

`cv.binomialRF`

is the cross-validated form of the `binomialRF`

, where K-fold crossvalidation is conducted to assess the feature's significance. Using the `cvFolds`

=K parameter, will result in a K-fold cross-validation where the data is 'chunked' into K-equally sized groups and then the averaged result is returned.

### Usage

```
.cv_binomialRF(X, y, cvFolds = 5, fdr.threshold = 0.05,
fdr.method = "BY", ntrees = 2000, keep.both = FALSE)
```

### Arguments

`X` |
design matrix |

`y` |
class label |

`cvFolds` |
how many times should we perform cross-validation |

`fdr.threshold` |
fdr.threshold for determining which set of features are significant |

`fdr.method` |
how should we adjust for multiple comparisons (i.e., |

`ntrees` |
how many trees should be used to grow the |

`keep.both` |
should we keep the naive binomialRF as well as the correlated adjustment |

### Value

a data.frame with 4 columns: Feature Name, cross-validated average for Frequency Selected, CV Median (Probability of Selecting it randomly), CV Median(Adjusted P-value based on `fdr.method`

), and averaged number of times selected as signficant.

### References

Zaim, SZ; Kenost, C.; Lussier, YA; Zhang, HH. binomialRF: Scalable Feature Selection and Screening for Random Forests to Identify Biomarkers and Their Interactions, bioRxiv, 2019.

### Examples

```
set.seed(324)
###############################
### Generate simulation data
###############################
X = matrix(rnorm(1000), ncol=10)
trueBeta= c(rep(10,5), rep(0,5))
z = 1 + X %*% trueBeta
pr = 1/(1+exp(-z))
y = as.factor(rbinom(100,1,pr))
###############################
### Run cross-validation
###############################
```

*binomialRF*version 0.1.0 Index]