Description Usage Arguments Details Author(s) See Also Examples

It produces a histogram of the response along with the estimated density from the assumed distribution as well as a normal Q-Q plot for the (randomised) normalised quantile response. It also provides the log-likelihood for AIC calculation, for instance.

1 2 3 4 5 |

`y` |
Response. |

`margin` |
The distributions allowed are: normal ("N"), normal where sigma2 corresponds to the standard deviation instead of the variance ("N2"), log-normal ("LN"), Gumbel ("GU"), reverse Gumbel ("rGU"), logistic ("LO"), Weibull ("WEI"), inverse Gaussian ("iG"), gamma ("GA"), Dagum ("DAGUM"), Singh-Maddala ("SM"), beta ("BE"), Fisk ("FISK"), Poisson ("PO"), zero truncated Poisson ("ZTP"), negative binomial - type I ("NBI"), negative binomial - type II ("NBII"), Poisson inverse Gaussian ("PIG"). |

`main` |
Title for the plot. |

`xlab` |
Title for the x axis. |

`print.par` |
If |

`plots` |
If |

`loglik` |
If |

`os` |
If |

`intervals` |
If |

`n.sim` |
Number of replicate datasets used to simulate quantiles of the residual distribution. |

`prob.lev` |
Overall probability of the left and right tails of the probabilities' distribution used for interval calculations. |

`i.f` |
Internal fitting option. This is not for user purposes. |

`...` |
Other graphics parameters to pass on to plotting commands. |

Prior to fitting a model with discrete and/or continuous margins, the distributions for the responses may be chosen by looking at the histogram of the response along with the estimated density from the assumed distribution, and at the normalised quantile responses. These will provide a rough guide to the adequacy of the chosen distribution. The latter are defined as the quantile standard normal function of the cumulative distribution function of the response with scale and location estimated by MLE. These should behave approximately as normally distributed variables (even though the original observations are not). Therefore, a normal Q-Q plot is appropriate here.

If `loglik = TRUE`

then this function also provides the log-likelihood for AIC calculation, for instance.

The shapiro test can also be performed.

Maintainer: Giampiero Marra [email protected]

1 | ```
## see examples in copulaReg
``` |

JRM documentation built on July 13, 2017, 5:03 p.m.

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