bayesPref {bayespref} | R Documentation |

This function implements a hierarchical Bayesian model for count data. Preference parameters are estimated using MCMC.

bayesPref(pData = NULL, mcmcL = 1000, dirvar = 2, calcdic = TRUE, constrain = FALSE, pmpriorLB = 1, pmpriorUB = 50, ppprior = NULL, dicburn = 100,indc = TRUE, pops = TRUE, pminit = NULL, ppinit = NULL, ipinit = NULL, constrainP = NULL, diradd = 0.1, univar = 2, estip = TRUE, measure = "mean")

`pData` |
A matrix of count data, rows are replicates or individuals and columns are categories. |

`mcmcL` |
A value indicating the length of the mcmc chain (recommended > 5000). |

`dirvar` |
A value for multiplier for population preference proposals. Increase to decrease proposal distances. |

`calcdic` |
A Boolean for returning DIC. |

`constrain` |
A Boolean for constraining population-level preferences to be equal. |

`pmpriorLB` |
A value setting the lower bounds of uniform prior for popmult. |

`pmpriorUB` |
A value setting the upper bounds of uniform prior for popmult. |

`ppprior` |
A vector of alphas for Dirichlet prior on population preference. |

`dicburn` |
A value indicating the number of burnin samples discarded for DIC calculation. |

`indc` |
A Boolean indicating an independence chain (default) vs. random-walk for populationlevel preferences. |

`pops` |
A Boolean indicating whether the first column of the matrix are values indicating populations. |

`pminit` |
A value indicating the initial value for the population multiplier. |

`ppinit` |
A vector or matrix of initial values population preferences. |

`ipinit` |
A vector or matrix of initial values for individual-level preferences. |

`constrainP` |
A vector with one entry per population giving the group each population belongs to. |

`diradd` |
A value added to the Dirichlet proposal for population preferences. |

`univar` |
A value that is the jump distance for univorm variance parameter. |

`estip` |
A boolean indicating whether to attempt to estimate individual preferences or only estimate population preference (the latter used a multivariate Polya). |

`measure` |
Indicates whether the "mean" or "median" is used for calculating DIC. |

Populations are indicated in the first column (if present) as integers. constrainP provides a way to group populations with the goal of comparing among various models. For example, if there are 3 populations in the data (indicated as 1, 2, 3) and it is desired to examine a model where populations 1 and 3 are constrained to have the same population-level preference parameters, constrainP=c(1,2,1).

The mixing of the chains should be observed by plotting each step in the chain against a population-level preference parameter, for example. Tuning parameters (e.g., dirvar), or initial starting conditions (e.g., ppinit) can be modified for better mixing if needed.

A list containing the following for each population in the analysis.

`IndPref` |
An array containing the individual-level preference parameter estimates for each step in the MCMC. |

`PopPref` |
An array containing the population-level preference parameter estimates for each step in the MCMC. |

`likelihood` |
The log-likelihood of the model given the parameter estimates for each step in the MCMC. |

`dic` |
The deviance information criterion score for the model. |

Even if only one population is present, the values are returned in a list of length one.

Zachariah Gompert zgompert@uwyo.edu, James A. Fordyce jfordyce@utk.edu

## Not run: data(YGGV) res <- bayesPref(pData=YGGV,mcmcL=1000) ## End(Not run)

[Package *bayespref* version 1.0 Index]