# Random Forest Cross-Valdidation for feature selection

### Description

This function shows the cross-validated prediction performance of models with sequentially reduced number of predictors (ranked by variable importance) via a nested cross-validation procedure.

### Usage

1 2 |

### Arguments

`trainx` |
matrix or data frame containing columns of predictor variables |

`trainy` |
vector of response, must have length equal to the number
of rows in |

`cv.fold` |
number of folds in the cross-validation |

`scale` |
if |

`step` |
if |

`mtry` |
a function of number of remaining predictor variables to
use as the |

`recursive` |
whether variable importance is (re-)assessed at each step of variable reduction |

`...` |
other arguments passed on to |

### Value

A list with the following components:

list(n.var=n.var, error.cv=error.cv, predicted=cv.pred)

`n.var` |
vector of number of variables used at each step |

`error.cv` |
corresponding vector of error rates or MSEs at each step |

`predicted` |
list of |

### Author(s)

Andy Liaw

### References

Svetnik, V., Liaw, A., Tong, C. and Wang, T., “Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules”, MCS 2004, Roli, F. and Windeatt, T. (Eds.) pp. 334-343.

### See Also

`RRF`

, `importance`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## The following can take a while to run, so if you really want to try
## it, copy and paste the code into R.
## Not run:
set.seed(647)
myiris <- cbind(iris[1:4], matrix(runif(508 * nrow(iris)), nrow(iris), 508))
result <- rrfcv(myiris, iris$Species)
with(result, plot(n.var, error.cv, log="x", type="o", lwd=2))
result <- replicate(5, rrfcv(myiris, iris$Species), simplify=FALSE)
error.cv <- sapply(result, "[[", "error.cv")
matplot(result[[1]]$n.var, cbind(rowMeans(error.cv), error.cv), type="l",
lwd=c(2, rep(1, ncol(error.cv))), col=1, lty=1, log="x",
xlab="Number of variables", ylab="CV Error")
## End(Not run)
``` |

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