Description Usage Arguments Details Value Note Author(s) Examples
This function implements a hierarchical Bayesian model for count data. Preference parameters are estimated using MCMC.
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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
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