indAggEi: Ecological Inference including Individual Data
In eiwild: Ecological Inference with individual and aggregate data

Description Usage Arguments Details Value See Also Examples

indAggEi (Individual and Aggregate Ecological Inference) Calculating ecological Inference including Individual data using the two functions convertEiData and runMBayes

indAggEi(form, aggr, indi = NULL, IDCols = c("ID"), 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)

`form`	`formula` in this format `cbind(column_1,column_2, ...,column_c)~cbind(row_1,row_2,...,row_r))`
`aggr`	`data.frame` with aggregate data. One district per line and one column giving one ID per district. (see Details)
`indi`	`data.frame` with individual data. One district per line and one column giving one ID per district. (see Details) If no individual data are present it defaults to `NULL`
`IDCols`	vector of length 2 giving the column-names or numbers of ID column
`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

indi is a districts x [(r*c)+1] data.frame containing one district per line. One column gives the ID of the districts which will be connection to the ID column in the aggr-data.frame.

For example a 2x3 ecological Inference problem with formula cbind(col1,col2,col3) ~ cbind(row1,row2) will have the row format : [ID, row1.col1, row1.col2, row1.col3, row2.col1, row2.col2, row2.col3]

It is important that the formula names correspond to the exact column number in the indi-data.frame.

The aggr data.frame can have more columns than the names given in formula as long as the colnames exist

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.

object of class "eiwild" which is a nested list with elements:

draws
- 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
rowdf original aggregate data
coldf original aggregate data

An Attribute called "seed" is also saved to reproduce the eiwild-object

convertEiData, runMBayes, mcmc tuneVars

## 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)
set.seed(1234)
res <- indAggEi(form=form, aggr=aggr, indi=indi, IDCols=c("ID","ID"),
               sample=1000, thinning=2, burnin=100,verbose=100)
res
summary(res)

# 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)