Description Usage Arguments Details Value Author(s) References Examples

Determine the probability of correct classification (PCC) for studies employing high dimensional features for classification; uses the method proposed by (Dobbin and Simon 2007) to choose the p-value threshold for feature selection.

1 | ```
ds_method(mu0, p, m, n, p1=0.5, lmax=1, ss=F, sampling.p)
``` |

`mu0` |
The effect size of the important features. |

`p` |
The number of the features in total. |

`m` |
The number of the important features. |

`n` |
The total sample size for the two groups. |

`p1` |
The prevalence of the group 1 in the population, default to 0.5. |

`lmax` |
The maximum eigenvalue of the variance-covariance matrix of the p features. Defaults to 1 which implies that the features are assumed i.i.d. |

`ss` |
Boolean variable, default to FALSE. The TRUE value instruct the program to compute the sensitivity and the specificity of the classifier. |

`sampling.p` |
The assumed proportion of group 1 samples in the training data; default of 0.5 assumes groups are equally represented regardless of p1. |

Refer to Dobbin and Simon (2007)

If ss=FALSE, the function returns the expected PCC. If ss=TRUE, the function returns a vector containing the expected PCC, sensitivity and specificity.

Meihua Wu <[email protected]> Brisa N. Sanchez <[email protected]> Peter X.K. Song <[email protected]> Raymond Luu <[email protected]> Wen Wang <[email protected]>

Dobbin, K.K., and Simon R.M. (2007). "Sample Size Planning for Developing Classifiers Using High-dimensional DNA Microarray Data." Biostatistics 8 (1): 101-117.

1 2 |

HDDesign documentation built on May 29, 2017, 8:13 p.m.

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