Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/rpt.poisGLMM.add.R

Calculates repeatability from a generalised linear mixed-effects models fitted by MCMC for count data.

1 | ```
rpt.poisGLMM.add(y, groups, CI=0.95, prior=NULL, verbose=FALSE, ...)
``` |

`y` |
Vector of a response values. |

`groups` |
Vector of group identities. |

`CI` |
Width of the Bayesian credible interval (defaults to 0.95). |

`prior` |
List of prior values passed to MCMCglmm function in MCMCglmm (see there for more details). Default priors will be used if prior is null. |

`verbose` |
Whether or not MCMCglmm should print MH diagnostics are printed to screen. Defaults to FALSE. |

`...` |
Additonal arguements that are passed on to MCMCglmm (e.g. length of chain, thinning interval). |

Models are fitted using the MCMCglmm function in MCMCglmm with `poisson`

family.
Models for binary data are fitted with `list(R=list(V=1e-10,nu=-1),G=list(G1=list(V=1,nu=1,alpha.mu=0,alpha.V=25^2)))`

unless other priors are specified in the call.

Returns an object of class rpt that is a a list with the following elements:

`datatype` |
Type of response (here: count). |

`method` |
Method used to calculate repeatability (here: MCMC). |

`CI` |
Width of the Bayesian credibility interval. |

`R.link` |
Point estimate for repeatability on the link scale, i.e. the mode of the posterior distribution. |

`se.link` |
Standard error ( |

`CI.link` |
Bayesian credibility interval for the repeatability on the link scale based on the posterior distribution of |

`P.link` |
Significance test for the link scale repeatability, returned as |

`R.org` |
Point estimate for repeatability on the original scale, i.e. the mode of the posterior distribution. |

`se.org` |
Standard error ( |

`CI.org` |
Bayesian credibility interval for repeatability on the original scale based on the posterior distribution of |

`P.org` |
Significance test for the original scale repeatability, returned as |

`R.post` |
Named list of MCMC samples form the posterior distributions. |

Holger Schielzeth ([email protected]) & Shinichi Nakagawa ([email protected])

Carrasco, J. L. (2010). *A generalized concordance correlation coefficient based on the variance components generalized linear mixed models with application to overdispersed count data*. Biometrics 66: 897-904.

Carrasco, J. L. and Jover, L. (2005). *Concordance correlation coefficient applied to discrete data*. Statistics in Medicine 24: 4021-4034.

Nakagawa, S. and Schielzeth, H. (2010) *Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists*. Biological Reviews 85: 935-956.

rpt.poisGLMM.multi, rpt, print.rpt

1 2 3 4 5 6 7 8 9 10 11 | ```
# repeatability for female clutch size over two years.
data(BroodParasitism)
attach(BroodParasitism)
(rpt.Host <- rpt.poisGLMM.add(OwnClutches, FemaleID))
detach(BroodParasitism)
# repeatability for male fledgling success
data(Fledglings)
attach(Fledglings)
(rpt.Fledge <- rpt.poisGLMM.add(Fledge, MaleID))
detach(Fledglings)
``` |

rptR documentation built on May 31, 2017, 2:53 a.m.

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