Description Usage Arguments Value Examples

Calculates the estimate and standard error of beta and phi. It takes as input counts from one group of samples for a single gene. This function is the core underlining function of the whole package. A significant part of the code is edited based on William H. Aeberhard's glmrob.nb R function; we appreciate them very much for sharing their code online. This function also implement Algorithm 1 of our submitted paper about DiPhiSeq. This function is called by robtest. Most users don't need to call this function directly.

1 2 3 | ```
robnb(y, log.depth, c.tukey.beta = 4, c.tukey.phi = 4, phi.ini = 0.5,
alpha = 0.2, minphi = 0.01, maxphi = 5, maxit = 30, maxit.beta = 30,
maxit.phi = 30, tol.beta = 0.01, tol.phi = 0.005)
``` |

`y` |
A count vector. |

`log.depth` |
Vector of log(sequencing depths). |

`c.tukey.beta` |
The c value for beta in Huber function. The default value should be appropriate for most datasets. |

`c.tukey.phi` |
The c value for phi in Huber function. The default value should be appropriate for most datasets. |

`phi.ini` |
The initial value of phi. |

`alpha` |
A positive value for setting initial values. The default value is usually appropriate. |

`minphi` |
A searching parameter for Algorithm 1 (check the algorithm for details.) The default value is usually appropriate. |

`maxphi` |
A searching parameter for Algorithm 1 (check the algorithm for details.) The default value is usually appropriate. |

`maxit` |
Maximum number of iterations for the outer loop. The default value is usually appropriate. |

`maxit.beta` |
Maximum number of iterations for the inner loop of solving beta. The default value is usually appropriate. |

`maxit.phi` |
Maximum number of iterations for the inner loop of solving phi. The default value is usually appropriate. |

`tol.beta` |
The numerical tolerance of solving beta. The default value is usually appropriate. |

`tol.phi` |
The numerical tolerance of solving phi. The default value is usually appropriate. |

A list that contains the elements:
`beta`

: the estimated (log) expression.
`phi`

: the estimated dispersion.
`fconv`

: flag of the convergence of the search.
`vars`

: the variance-covariance matrix of the estimates.
`sd.beta`

: the standard error of beta.
`sd.phi`

: the standard error of phi.
`y`

: the input y value.
`log.depth`

: log(sequencing depth).

1 2 3 |

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