Density, distribution function, quantile function, random generation, standard deviation and Anscombe residuals for some count data distributions. These auxiliary functions are used by several functions of the `tscount`

package.

1 2 3 4 5 6 7 | ```
ddistr(x, meanvalue, distr=c("poisson", "nbinom"), distrcoefs, ...)
pdistr(q, meanvalue, distr=c("poisson", "nbinom"), distrcoefs, ...)
qdistr(p, meanvalue, distr=c("poisson", "nbinom"), distrcoefs, ...)
rdistr(n, meanvalue, distr=c("poisson", "nbinom"), distrcoefs)
sddistr(meanvalue, distr=c("poisson", "nbinom"), distrcoefs)
ardistr(response, meanvalue, distr=c("poisson", "nbinom"), distrcoefs)
checkdistr(distr=c("poisson", "nbinom"), distrcoefs)
``` |

`x` |
vector of (non-negative integer) quantiles. |

`q` |
vector of quantiles. |

`p` |
vector of probabilities. |

`n` |
positive integer value giving the number of random values to return. |

`response` |
vector of true observations for calculation of residuals. |

`meanvalue` |
non-negative numeric vector of means. |

`distr` |
character value giving the distribution. Possible values are currently |

`distrcoefs` |
vector of additional distribution coefficients. For the Poisson distribution this argument can be omitted. For the negative binomial distribution it needs to be a vector of length one giving the value for the parameter |

`...` |
additional arguments |

Basically, these function are wrappers for specific functions for the respective distribution. The function `ddistr`

gives the density of the specified distribution, `pdistr`

the distribution function, `qdistr`

the quantile function and `rdistr`

generates random deviates from this distribution. These functions are a generalisation of the respective functions where `distr`

is replaced by either `pois`

or `nbinom`

. The function `sddistr`

returns the standard deviation of the specified distribution. The function `ardistr`

calculates Anscombe residuals for given values of the response. The function `checkdistr`

is for verification of the arguments `distr`

and `distrcoefs`

.

Tobias Liboschik

`Poisson`

for the Poisson distribution and `NegBinomial`

for the negative binomial distribution.

`tsglm`

for fitting a more genereal GLM for time series of counts.

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

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