R: Regression assuming a Skellam distribution

skellam.reg {skellam} | R Documentation

## Regression assuming a Skellam distribution

### Description

Regression assuming a Skellam distribution.

### Usage

    skellam.reg(y, x)

### Arguments

`y` | A vector of integers, positive or negative.
`x` | A matrix, a vector or a data.frame with the covariates.

### Details

We use the exponential link function to ensure that the both λ_s are positive. The command `nlm` does the main job.

### Value

A list including:

`loglik` | The maximised log-likelihood value.
`param1` | The estimated regression coefficients of λ_1. This is matrix, with the first column being the estimated regression coefficients. The second column is their relevant standard error. The third column is the t value (coef/se(coef)) and the final column is the p-value of the Wald test.
`param2` | The estimated regression coefficients of λ_2. This is matrix, with the first column being the estimated regression coefficients. The second column is their relevant standard error. The third column is the t value (coef/se(coef)) and the final column is the p-value of the Wald test.

### Author(s)

Michail Tsagris

### References

Skellam, J. G. (1946) The frequency distribution of the difference between two Poisson variates belonging to different populations. Journal of the Royal Statistical Society, series A 109/3, 26.

Strackee, J.; van der Gon, J. J. D. (1962) The frequency distribution of the difference between two Poisson variates. Statistica Neerlandica 16/1, 17-23.

Karlis and Ntzoufras IMA 2009 presentation http://stat-athens.aueb.gr/~jbn/papers/files/20_Karlis_Ntzoufras_2009_IMA_presentation_handouts_v01.pdf

### Examples

    require('skellam')

    set.seed(0)

    x <- rnorm(1000)
    y1 <- rpois(1000, exp(1 + 1 * x) )
    y2 <- rpois(1000 , exp(-1 + 1 * x) )
    y <- y2 - y1
    skellam.reg(y, x)

[Package skellam version 0.2.0 Index]