runMBayes: Metropolis Algorithm for ecological Inference
In eiwild: Ecological Inference with individual and aggregate data

Description Usage Arguments Details Value See Also Examples

This function is a wrapper function which calls the appropriate Metropolis algorithm depending on the hyperpriori which is chosen.

runMBayes(convList, whichPriori = "gamma", prioriPars = list(shape = 4, rate
  = 2), startValsAlpha = NULL, startValsBeta = NULL, betaVars = NULL,
  alphaVars = NULL, sample, burnin = 0, thinning = 1, verbose = 1,
  retBeta = FALSE, seed = NULL)

`convList`	output of `convertEiData`-function.
`whichPriori`	character string specifying the chosen hyperpriori. Options are `"gamma"` or `"expo"` (see Details)
`prioriPars`	vector or matrix of parameters for the specified hyperpriori in `whichPriori`
`startValsAlpha`	matrix with dimension `c(rows,columns)` giving the starting values for alpha. If `NULL` random numbers of rdirichlet with chosen hyperpriori will be chosen.
`startValsBeta`	array with dimension `c(rows,columns,districts)` giving the starting values of beta If `NULL` random multinomial numbers will be chosen.
`sample`	the sample size to be saved in output. Total length of chain will be `burnin` `+` `sample` `*` `thinning`
`burnin`	number of draws to be cut away from the beginning of the Markov-Chain. `default=0`
`thinning`	number specifying the thinning interval. `default=1`
`verbose`	an integer specifying whether the progress of the sampler is printed to the screen (defaults to 0). If verbose is greater than 0, the iteration number is printed to the screen every verboseth iteration
`betaVars`	array-object with dimensions `(rows, columns-1, districts)` giving variance of proposal density for β-values
`alphaVars`	matrix of dimensions `(rows, columns)` giving variance of proposal density for α-values.
`retBeta`	logical `TRUE` if estimated β-parameters should be returned. With large number of precincts there can be problems with memory
`seed`	Default is `NULL`. Can be given the `"seed"`-attribute of an `eiwild`-object to reproduce an `eiwild`-object

The whichPriori-parameter has the options "gamma" or "expo" and corresponding prioriPars-parameters in a "list":

"expo" and numeric list-element called "lam" corresponding to: α_{rc} \sim Exp(λ)
"expo" and matrix list-element called "lam" corresponding to: α_{rc} \sim Exp(λ_{rc})
"gamma" and two numeric list-element called "shape" and "rate" corresponding to: α_{rc} \sim Gamma(λ_1, λ_2)
"gamma" and two matrix list-element called "shape" and "rate" corresponding to: α_{rc} \sim Gamma(λ_1^{rc}, λ_2^{rc})

The "seed" attribute is generated by the .Random.seed-function.

list-Object with elements:

alphaDraws mcmc-object
cellCounts mcmc-object
betaDraws mcmc-object
betaAcc "numeric" with Acceptance ratios
alphaAcc "numeric" with Acceptance ratios
alphaVars matrix with variances for proposal density
betaVars array with variances for proposal density

convertEiData, runMBayes, mcmc tuneVars, indAggEi

## Not run: 
# loading some fake election data
data(topleveldat)
form <- cbind(CSU_2, SPD_2, LINK_2, GRUN_2) ~ cbind(CSU_1, SPD_1, Link_1)
conv <- convertEiData(form=form, aggr=aggr, indi=indi, IDCols=c("ID","ID"))
set.seed(1234)
res <- runMBayes(conv, sample=1000, thinning=2, burnin=100,verbose=100)

## !!! not an eiwild object !!!
class(res)

# better to use indAggEi
set.seed(12345)
res2 <- indAggEi(form=form, aggr=aggr, indi=indi, IDCols=c("ID","ID"),
                 sample=1000, thinning=2, burnin=100,verbose=100)
class(res2)
summary(res2)

# with individual alpha-hyperpriori-parameters
hypMat <- list(shape = matrix(c(30,4,4,4,
                                4,30,4,4,
                                4,4,30,4), nrow=3, ncol=4, byrow=TRUE),
               rate = matrix(c(1,2,2,2,
                               2,1,2,2,
                               2,2,1,2), nrow=3, ncol=4, byrow=TRUE))
set.seed(12345)
res2 <- indAggEi(form=form, aggr=aggr, indi=indi, IDCols=c("ID","ID"),
                 sample=1000, thinning=2, burnin=100, verbose=100,
                 prioriPars=hypMat, whichPriori="gamma")

## End(Not run)