View source: R/regression_models.R

Binomial regression | R Documentation |

Binomial regression.

```
binom.reg(y, ni, x, full = FALSE, tol = 1e-07, maxiters = 100)
```

`y` |
The dependent variable; a numerical vector with integer values, 0, 1, 2,... The successes. |

`ni` |
A vector with integer values, greater than or equal to y. The trials. |

`x` |
A matrix with the data, where the rows denote the samples (and the two groups) and the columns are the variables. This can be a matrix or a data.frame (with factors). |

`full` |
If this is FALSE, the coefficients and the deviance will be returned only. If this is TRUE, more information is returned. |

`tol` |
The tolerance value to terminate the Newton-Raphson algorithm. |

`maxiters` |
The max number of iterations that can take place in each regression. |

The difference from logistic regression is that in the binomial regression the binomial distribution is used and not the Bernoulli.

When full is FALSE a list including:

`be` |
The regression coefficients. |

`devi` |
The deviance of the model. |

When full is TRUE a list including:

`info` |
The regression coefficients, their standard error, their Wald test statistic and their p-value. |

`devi` |
The deviance. |

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

McCullagh Peter and John A. Nelder. Generalized linear models. CRC Press, USA, 2nd edition, 1989.

` negbin.reg, hp.reg, ztp.reg `

```
x <- matrix(rnorm(100 * 2), ncol = 2)
y <- rbinom(100, 20, 0.5) ## binary logistic regression
ni <- rep(20, 100)
a <- binom.reg(y, ni, x, full = TRUE)
x <- NULL
```

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