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BayesMDei <- function(formula, data, total, lambda1 = 4, lambda2 = 2,
tune.alpha = NULL, tune.beta = NULL,
start.alphas = NULL, start.betas = NULL,
sample = 1000, thin = 1, burnin = 1000, verbose = 0,
ret.beta ='r', ret.mcmc = TRUE, ...){
if(thin < 1){stop('thin must be positive integer')}
if(sample < 1){stop('thin must be positive integer')}
if(burnin < 0){stop('burnin must be non-negative integer')}
DD <- model.frame(formula, data)
countParty <- countGroup <- propParty <- propGroup <- FALSE
checkGroups <- round(apply(DD[[2]], 1, sum), 3)
checkParties <- round(apply(DD[[1]], 1, sum), 3)
if(all(DD[[1]] %% 1 == 0) & all(DD[[1]] >= 0)){countParty <- TRUE}
else if(all(0 <= DD[[1]] & DD[[1]] <= 1)){
if(all(checkParties == 1)){propParty <- TRUE}else{
stop("column marginals are proportions that do not
sum to 1 - please respecify data")}}
else stop("column marginals are neither counts nor proportions - please
respecify data")
if(all(DD[[2]] %% 1 == 0) & all(DD[[2]] >= 0)){countGroup <- TRUE}
else if(all(0 <= DD[[2]] & DD[[2]] <= 1)){
if(all(checkGroups == 1)){propGroup <- TRUE}else{
stop("row marginals are proportions that do not sum to 1 - please
respecify data")}}
else stop("row marginals are neither counts nor proportions - please
respecify data")
if((propParty | propGroup) & is.null(total)){
stop("one or both marginals are proportions - 'total' must be
provided")}
if(propParty & !is.null(total)){
DD[[1]] <- DD[[1]] * total
warning("column marginals are proportions - multiplying by unit size")}
if(propGroup & !is.null(total)){
DD[[2]] <- DD[[2]] * total
warning("row margnials are proportions - multiplying by unit size")}
checkGroups <- round(apply(DD[[2]], 1, sum), 1)
checkParties <- round(apply(DD[[1]], 1, sum), 1)
if(identical(checkParties, checkGroups) == FALSE){
stop("row and column totals unequal in some units - please
respecify data")}
Groups <- DD[[2]]
TT <- t(DD[[1]])
XX <- t(Groups/apply(Groups,1,sum))
group.names <- colnames(Groups)
party.names <- rownames(TT)
RR <- t(Groups)
NG <- nrow(XX)
NP <- nrow(TT)
Precincts <- nrow(DD)
if(is.null(start.alphas)){
start.alphas <- matrix(rgamma(NG*NP, lambda1, lambda2), NG, NP)}
if(min(start.alphas) <= 0){stop('inadmissable starting values for alpha')}
if(is.null(start.betas)){
start.betas <- array(NA, dim= c(NG, NP, Precincts))
for(i in 1:Precincts){
start.betas[,,i] <- rdirichlet(NG, rep(1,NP))}
}
if(identical(round(apply(start.betas, c(1,3), sum),10),
matrix(1,NG, Precincts))!=TRUE){
stop('inadmissable starting values for beta')}
if(is.null(tune.alpha)){
tune.alpha <- matrix(rep(.25,NG*NP), NG, NP)}
if(is.null(tune.beta)){
tune.beta <- array(rep(.05, NG*(NP-1)*Precincts), c(NG, NP-1, Precincts))}
tune.alpha <- as.matrix(tune.alpha)
if(identical(dim(tune.alpha), c(NG, NP))!=TRUE) {stop("'tune.alpha'
has incorrect dimensions")}
if(identical(as.numeric(dim(tune.beta)), c(NG, NP-1, Precincts))!=TRUE)
{stop("'tune.beta'
has incorrect dimensions")}
beta.names <- paste(paste(paste(group.names,matrix(rep(party.names,
NG),NG,NP, byrow=T)
,sep="."),
matrix(rep(1:Precincts,NG*NP),NG*NP, Precincts,
byrow=TRUE),sep="."), ".txt.gz", sep="")
if(ret.beta == 's'){touch.betas(beta.names)
ret.beta <- 2}
if(ret.beta == 'd'){ret.beta <- 1}
if(ret.beta == 'r'){ret.beta <- 0}
if(is.numeric(ret.beta)==FALSE){stop('incorrect option for
ret.beta')}
output <- .Call("rbycei_fcn1",
as.numeric(start.alphas),
as.numeric(start.betas),
as.numeric(TT),
as.numeric(XX),
as.numeric(tune.alpha),
as.numeric(tune.beta),
as.integer(NG),
as.integer(NP),
as.integer(Precincts),
as.numeric(lambda1),
as.numeric(lambda2),
as.integer(sample),
as.integer(thin),
as.integer(burnin),
as.integer(verbose),
as.integer(ret.beta),
as.numeric(RR),
as.character(beta.names)
)
if(ret.beta==0){names(output) <- c("Alpha", "Beta", "alpha.acc",
"beta.acc", "cell.count")}
else{names(output) <- c("Alpha", "alpha.acc", "beta.acc","cell.count")}
if(ret.mcmc){
colnames(output$Alpha) <- paste("alpha",matrix(rep(group.names,
NP),NG,NP)
,matrix(rep(party.names, NG),NG,NP,
byrow=T) ,sep=".")
output$Alpha <- coda::mcmc(output$Alpha, thin=thin)
colnames(output$cell.count) <- paste("ccount",matrix(rep(group.names,
NP),NG,NP)
,matrix(rep(party.names, NG),NG,NP,
byrow=T) ,sep=".")
output$cell.count <- coda::mcmc(output$cell.count, thin=thin)
if(ret.beta==0){
colnames(output$Beta) <- paste(paste("beta", group.names,matrix(rep(party.names, NG),NG,NP, byrow=T) ,sep="."), matrix(rep(1:Precincts,NG*NP),NG*NP, Precincts, byrow=TRUE),sep=".")
output$Beta <- coda::mcmc(output$Beta, thin=thin)
}
}else{
output$Alpha <- array(t(output$Alpha), c(NG, NP, sample))
dimnames(output$Alpha) <- list(group.names, party.names, 1:sample)
output$cell.count <- array(t(output$cell.count), c(NG, NP, sample))
dimnames(output$cell.count) <- list(group.names, party.names, 1:sample)
if(ret.beta==0){
output$Beta <- array(t(output$Beta), c(NG, NP, Precincts, sample))
dimnames(output$Beta) <- list(group.names, party.names, 1:Precincts,
1:sample)
}
}
return(output)
}
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