Hellinger distance based univariate regression for proportions | R Documentation |

Hellinger distance based univariate regression for proportions.

```
prophelling.reg(y, x, cov = FALSE, tol = 1e-07, maxiters = 100)
```

`y` |
The dependent variable, a numerical vector with percentages. |

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

`cov` |
Should the sandwich covariance matrix and the standard errors be returned? If yes, set this equal to TRUE. |

`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 Jensen-Shannon divergence 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 beta coefficients, if the input argument argument was set to TRUE. |

`covb` |
The sandwich covariance matrix of the beta coefficients, if the input argument argument was set to TRUE. |

`js` |
The final Jensen-Shannon divergence. |

`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.

Tsagris, Michail (2015). A novel, divergence based, regression for compositional data. Proceedings of the 28th Panhellenic Statistics Conference, 15-18/4/2015, Athens, Greece. https://arxiv.org/pdf/1511.07600.pdf

` propols.reg, simplex.mle, kumar.mle `

```
y <- rbeta(150, 3, 4)
x <- iris
a <- prophelling.reg(y, x)
```

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