###### Description

asr_families specifies the details of the models accepted by the `family`

argument to `asreml`

.

###### Usage

`asr_gaussian(link = "identity", dispersion = NA)`

`asr_Gamma(link = "inverse", dispersion = 1, phi = 1)`

`asr_inverse.gaussian(link = "1/mu^2", dispersion = NA)`

`asr_binomial(link = "logit", dispersion = 1, total = NULL)`

`asr_multinomial(link = "logit", dispersion = 1, total = NULL)`

`asr_negative.binomial(link = "log", dispersion = 1, phi = 1)`

`asr_poisson(link = "log", dispersion = 1)`

###### Arguments

`link` |
A character string identifying the link function; valid values are:Gaussian: identity, log, inverseGamma: identity, log, inverseinverse.gaussian: 1/mu^2, identity, log, inversebinomial: logit, probit, cloglogmultinomial: logit, probit, cloglognegative.binomial: identity, log, inversepoisson: identity, log, sqrt |

`dispersion` |
If `NA` , the default for Gaussian and inverse Gaussian models, the dispersion parameter is estimated, otherwise it is ﬁxed at the nominated value (default 1.0). |

`phi` |
The known value of the additional parameter `phi` . |

`total` |
A character string or name giving the column in `data` containing the totalcounts. |

###### Value

A list of functions and expressions needed by the `family`

argument.

###### Functions

`asr_gaussian:` |
The Gaussian model (default). |

`asr_Gamma` |
The gamma model. |

`asr_inverse.gaussian` |
The inverse Gaussian model. |

`asr_binomial` |
The binomial model. If the response is between 0 and 1 it is interpreted as the proportion of successes, otherwise, if not a binary (0,1) variate, it is interpreted as counts of successes; the total number of cases is given by the `total` argument. If `total` is `NULL` , a binary (0,1) response is expected. |

`asr_multinomial` |
The multinomial model. The response can either be a matrix of counts with the response categories as columns, with an additional column for the total number of cases in each row, or in univariate style with the response as a factor. If the response is a matrix and `total=NULL` , the total counts are calculated from the category columns. |

`asr_negative.binomial` |
The negative-binomial model. |

`asr_poisson` |
The poisson model. |