Description Usage Arguments Details Value Author(s) References See Also Examples

Given a model classifier and a data set, this function performs cross-validation by repeatedly splitting the data into training and testing subsets in order to estimate the performance of this kind of classifer on new data.

1 | ```
CrossValidate(model, data, status, frac, nLoop, prune=keepAll, verbose=TRUE)
``` |

`model` |
An element of the |

`data` |
A matrix containing the data to be used for cross-validation. As with most gene expression data, columns are the independent samples or observations and rows are the measured features. |

`status` |
A binary-valued factor with the classes to be predicted. |

`frac` |
A number between 0 and 1; the fraction of the data that should be used in each iteration to train the model. |

`nLoop` |
An integer; the number of times to split the data into training and test sets. |

`prune` |
A function that takes two inoputs, a data matrix and a factor with two levels, and rteturns a logical vector whose length equals the number of rows in the data matrix. |

`verbose` |
A logical value; should the cross-validation routine report interim progress. |

The `CrossValidate`

package provides generic tools for performing
cross-validation on classificaiton methods in the context of
high-throughput data sets such as those produced by gene expression
microarrays. In order to use a classifier with this implementaiton of
cross-validation, you must first prepare a pair of functions (one for
learning models from training data, and one for making predictions on
test data). These functions, along with any required meta-parameters,
are used to create an object of the `Modeler-class`

. That
object is then passed to the `CrossValidate`

function along
with the full training data set. The full data set is then repeatedly
split into its own training and test sets; you can specify the
fraction to be used for training and the number of iterations. The
result is a detailed look at the accuracy, sensitivity, specificity,
and positive and negative predictive value of the model, as estimated
by cross-validation.

An object of the `CrossValidate-class`

.

Kevin R. Coombes [email protected]

See the manual page for the `CrossValidate-package`

for a list
of related references.

See the manual page for the `CrossValidate-package`

for a list
of classifiers that have been adapted to work with this
cross-validation mechanism.

See `CrossValidate-class`

for a description of the slots in
the object created by this function.

1 2 3 4 5 6 |

CrossValidate documentation built on Aug. 4, 2017, 3:01 p.m.

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