Description Usage Arguments Value Author(s) References Examples

A commonly used method with microarrays to select the best genes for class prediction is implemented. This method involves computing the one-way anova for each gene and select the genes with the between classes sum of squares or equivalently the largest F-ratios.

1 | ```
featureSelect(X, y, numFeatures = 10)
``` |

`X` |
data matrix |

`y` |
must be a factor with length equal to the number of rows of X |

`numFeatures` |
the number of features to be selected - usually larger than the default 10. |

the column indicies corresponding to the columns of X that are selected

A. I. McLeod

tba

1 2 3 4 5 6 | ```
Xy <- churnTrain
y <- Xy[, ncol(Xy)]
Xy <- Xy[, -ncol(Xy)]
X <- as.matrix.data.frame(Xy[,-(1:5)])
(ind <- featureSelect(X, y, numFeatures=5))
colnames(X)[ind]
``` |

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