irls.nb.1 | R Documentation |

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

irls.nb.1(y, s, x, phi, beta0 = rep(NA, p), mustart = NULL, maxit = 50, tol.mu = 0.001/length(y), print.level = 0)

`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? |

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