View source: R/regression_models.R

Hellinger distance based regression for count data | R Documentation |

Hellinger distance based regression for count data.

```
hellinger.countreg(y, x, tol = 1e-07, maxiters = 100)
```

`y` |
The dependent variable, a numerical vector with integer valued data, counts. |

`x` |
A numerical matrix with the indendent variables. We add, internally, the first column of ones. |

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

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

We minimise the Hellinger distance instead of the ordinarily used divergence, the Kullback-Leibler.
Both of them fall under the `\phi`

-divergence class models and hance this one produces asympottically
normal regression coefficients as well.

A list including:

`be` |
The regression coefficients. |

`seb` |
The sandwich standard errors of the coefficients. |

`covbe` |
The sandwich covariance matrix of the regression coefficients. |

`H` |
The final Hellinger distance. |

`iters` |
The number of iterations required by Newton-Raphson. |

Michail Tsagris.

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

` negbin.reg, gee.reg `

```
y <- rpois(100, 10)
x <- iris[1:100, 1]
a <- hellinger.countreg(y, x)
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.