Description Usage Arguments Details Value Author(s)

Outlier detection and robust regression through an iterative penalized regression with tuning parameter chosen by modified BIC

1 | ```
IPOD(X, Y, H, method = "hard", TOL = 1e-04, length.out = 50)
``` |

`X` |
an N by k design matrix |

`Y` |
an N by 1 response |

`H` |
an N by N projection matrix |

`method` |
a string, if method = "hard", hard thresholding is applied; if method = "soft", soft thresholding is applied |

`TOL` |
relative iterative converence tolerance, default to 1e-04 |

`length.out` |
A numeric, number of candidate tuning parameter lambda under consideration for further modified BIC model selection, default to 50. |

If there is no predictors, set `X = NULL`

.

Y = X beta + gamma + sigma epsilon

Y is N by 1 reponse vector, X is N by k design matrix, beta is k by 1 coefficients, gamma is N by 1 outlier indicator, sigma is a scalar and the noise standard deviation and epsilon is N by 1 vector with components independently distributed as standard normal N(0,1).

`gamma` |
a vector of length N, estimated outlier indicator gamma |

`resOpt.scale` |
a vector of length N, test statistics for each of the N genes |

`p` |
a vector of length N, p-values for each of the N genes |

Yunting Sun yunting.sun@gmail.com, Nancy R.Zhang nzhang@stanford.edu, Art B.Owen owen@stanford.edu

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.