Description Usage Arguments Value Note Author(s) See Also Examples

The distribution of Kolmogorov Rank Sum scores generated by this package depends on a variety of factors including the size of the signature and the total number of genes measured in each sample. For a given classifier, this function bootstraps an approximate distribution for the scores and then identifies optimum parameters for the gamma distribution that best fits the bootstrap distribution. The corresponding gamma probability function is then returned, allowing p-values for one or more scores to be readily computed.

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`eset` |
An |

`class` |
A factor with two or more levels indicating which class each sample in the expression set belongs OR an integer indicating which column of pData(eset) contains this information. |

`n` |
The number of genes per class to use in the bootstrap signature. |

`samples` |
The number of bootstrap samples to generate. A value of at least 1000 give good results, but may take a a while. |

`type` |
One of "up", "down", or "both".
See |

`rescale` |
Logical indicating whether scores should be rescaled to range c(0,1). |

`verbose` |
Set to TRUE if you want more evidence of progress while data is being processed. Set to FALSE if you want your CPU cycles to be used on analysis and not printing messages. |

`method` |
One of either 'kort' or 'yang'. Should match that
used for |

A function (1-pgamma(x, ...)) with the appropriate parameters preset based on log likelihood maximization relative to the bootstrapped distribution.

All arguments should match those used by `dksClassify`

,
otherwise the estimated p-values will not meaningfully describe
the distribution of scores generated by that function.

Eric J. Kort

`dksTrain`

, `dksSelectGenes`

,
`dksClassify`

, `DKSGeneScores`

,
`DKSPredicted`

,
`DKSClassifier`

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