Estimate the regression coefficients in an NB GLM model with known dispersion parameters

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`y` |
an n vector of counts |

`s` |
a scalar or an n vector of effective library sizes |

`x` |
an n by p design matrix |

`phi` |
a scalar or an n-vector of dispersion parameters |

`mustart` |
starting values for the vector of means |

`beta0` |
a vector specifying known and unknown components of the regression coefficients: non-NA components are hypothesized values of beta, NA components are free components |

`maxit` |
maximum number of iterations |

`tol.mu` |
a number, convergence criteria |

`print.level` |
a number, print level |

This function estimates the regression coefficients using
iterative reweighted least squares (IRLS) algorithm, which
is equivalent to Fisher scoring. The implementation is
based on `glm.fit`

.

Users can choose to fix some regression coefficients by
specifying `beta0`

. (This is useful when fitting a
model under a null hypothesis.)

a list of the following components:

`beta` |
a p-vector of estimated regression coefficients |

`mu` |
an n-vector of estimated mean values |

`conv` |
logical. Was the IRLS algorithm judged to have converged? |

`zero` |
logical. Was any of the fitted mean close to 0? |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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