Description Usage Arguments Value Author(s) References Examples

Determine the probability of correct classification (PCC) for a study employing the ideal classifier. The ideal classifier is constructed assuming we know exactly the important features and their effect size. The ideal PCC is the uppper bound of the PCC of any linear classifiers.

1 | ```
ideal_pcc(mu0, m, p1 = 0.5)
``` |

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

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

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

The PCC of the ideal classifier.

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

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

1 2 | ```
ideal_pcc(mu0=0.4, m=10, p1 = 0.6)
#return: 0.8999055
``` |

```
[1] 0.8999055
```

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