A simplified version of `glm`

that does only parameter estimation

1 |

`predictors` |
The columns of the standard design matrix to include in the model. For example, "c1", "c2" for main effects, and "c12" for interactions. |

`data` |
A design matrix with cell counts included |

`normalized` |
Logical: If TRUE, include a normalization step after coefficient estimation, which resets the value of the intercept so that the sum of predicted values is exactly 1 |

`precision` |
Controls the precision of the coefficient estimates. A higher number is less precise. 1 corresponds to machine epsilon. |

Maximize the Poisson likelihood using BFGS in `optim()`

.

The vector of estimated log-linear coefficients. The first
coefficient is the intercept, and the remaining ones correspond to the
`predictors`

argument, in that order

Zach Kurtz

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

All documentation is copyright its authors; we didn't write any of that.