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BayesMDei4cov <- function(formula, covariate, total, data, lambda1 = 2,
lambda2 = 4, covariateprior = NULL,
tune.dr = NULL, tune.beta = NULL, tune.gamma
= NULL, tune.delta = NULL,
start.dr = NULL, start.betas = NULL,
start.gamma = NULL, start.delta = NULL,
sample = 1000,
thin = 1, burnin = 1000, verbose = 0, ret.beta =
'r', ret.mcmc = TRUE, usrfun = NULL, ...){
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 margnials 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)
CC <- model.frame(covariate, data)
ZZ <- as.matrix(CC)
NG <- nrow(XX)
NP <- nrow(TT)
Precincts <- nrow(DD)
if(is.null(start.dr)){
start.dr <- matrix(rgamma(NG, lambda1, lambda2), NG)}
if(min(start.dr) <= 0){stop("inadmissable starting values for dr")}
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(start.gamma)){
start.gamma <- cbind(matrix(rnorm(NG*(NP-1)), NG, NP-1),0)}
if(identical(start.gamma[,NP], rep(0,NG))!=TRUE){stop("final column
of 'start.gamma' must be zero")}
if(is.null(start.delta)){
start.delta <- cbind(matrix(rnorm(NG*(NP-1)), NG, NP-1),0)}
if(identical(start.delta[,NP], rep(0,NG))!=TRUE){stop("final column
of 'start.delta' must be zero")}
usrenv <- environment(fun = usrfun)
usrlen <- length(as.numeric(usrfun(list(start.dr, start.betas,
start.gamma, start.delta, TT,
RR))))
if(is.null(tune.dr)){
tune.dr <- rep(2,NG)}
if(is.null(tune.beta)){
tune.beta <- array(rep(.05, NG*(NP-1)*Precincts), c(NG, NP-1, Precincts))}
if(is.null(tune.gamma)){
tune.gamma <- matrix(.25, NG, NP-1)}
if(is.null(tune.delta)){
tune.delta <- matrix(.25, NG, NP-1)}
if(identical(length(tune.dr), NG)!=TRUE) {stop("'tune.dr'
has incorrect dimensions")}
if(identical(as.numeric(dim(tune.beta)), c(NG, NP-1, Precincts))!=TRUE)
{stop("'tune.beta'
has incorrect dimensions")}
if(identical(as.numeric(dim(tune.gamma)), c(NG, NP-1))!=TRUE)
{stop("'tune.gamma'
has incorrect dimensions")}
if(identical(as.numeric(dim(tune.delta)), c(NG, NP-1))!=TRUE)
{stop("'tune.delta'
has incorrect dimensions")}
if(is.null(covariateprior)){
covprior <- 0
delmean <- gammean <- rep(0, NG*(NP-1))
delsd <- gamsd <- rep(1, NG*(NP-1))
}else{
covprior <- 1
delmean <- covariateprior[[1]]
delsd <- covariateprior[[2]]
gammean <- covariateprior[[3]]
gamsd <- covariateprior[[4]]
if(identical(as.numeric(dim(delmean)), c(NG, NP-1))!=TRUE)
{stop("matrix of prior means for delta has incorrect dimensions")}
if(identical(as.numeric(dim(delsd)), c(NG, NP-1))!=TRUE)
{stop("matrix of prior sd for delta has incorrect dimensions")}
if(identical(as.numeric(dim(gammean)), c(NG, NP-1))!=TRUE)
{stop("matrix of prior means for gamma has incorrect dimensions")}
if(identical(as.numeric(dim(gamsd)), c(NG, NP-1))!=TRUE)
{stop("matrix of prior sd for gamma has incorrect dimensions")}
if(min(gamsd)<=0)
{stop("prior sd for gamma must be > 0")}
if(min(gamsd)<=0)
{stop("prior sd for delta must be > 0")}
}
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_fcn4",
as.numeric(start.dr),
as.numeric(start.betas),
as.numeric(start.gamma),
as.numeric(start.delta),
as.numeric(TT),
as.numeric(XX),
as.numeric(ZZ),
as.numeric(tune.dr),
as.numeric(tune.beta),
as.numeric(tune.gamma),
as.numeric(tune.delta),
as.integer(NG),
as.integer(NP),
as.integer(Precincts),
as.numeric(lambda1),
as.numeric(lambda2),
as.integer(covprior),
as.numeric(delmean),
as.numeric(delsd),
as.numeric(gammean),
as.numeric(gamsd),
as.integer(sample),
as.integer(thin),
as.integer(burnin),
as.integer(verbose),
as.integer(ret.beta),
as.numeric(RR),
usrfun,
usrenv,
as.integer(usrlen),
as.character(beta.names)
)
if(ret.beta==0){names(output) <- c("Dr", "Beta","Gamma","Delta",
"dr.acc","beta.acc", "gamma.acc",
"delta.acc","cell.count", "usrfun")}
else{names(output) <- c("Dr","Gamma","Delta",
"dr.acc","beta.acc", "gamma.acc",
"delta.acc","cell.count", "usrfun")}
if(ret.mcmc){
colnames(output$Dr) <- paste("dr", group.names, sep=".")
output$Dr <- coda::mcmc(output$Dr, thin=thin)
colnames(output$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)
colnames(output$Gamma) <- paste("gamma",matrix(rep(group.names,
(NP-1)),NG,NP-1)
,matrix(rep(party.names[1:(NP-1)], NG),NG,NP-1,
byrow=T) ,sep=".")
output$Gamma <- coda::mcmc(output$Gamma, thin=thin)
colnames(output$Delta) <- paste("delta",matrix(rep(group.names,
(NP-1)),NG,NP-1)
,matrix(rep(party.names[1:(NP-1)], NG),NG,NP-1,
byrow=T) ,sep=".")
output$Delta <- coda::mcmc(output$Delta, 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$Beta, thin=thin)
}
}else{
output$Dr <- t(output$Dr)
dimnames(output$Dr) <- list(paste("dr", group.names, sep="."), 1:sample)
output$cell.count <- array(t(output$cell.count), c(NG, NP, sample))
dimnames(output$cell.count) <- list(group.names, party.names,
1:sample)
output$Gamma <- array(t(output$Gamma), c(NG, NP-1, sample))
dimnames(output$Gamma) <- list(group.names, party.names[1:(NP-1)], 1:sample)
output$Delta <- array(t(output$Delta), c(NG, NP-1,sample))
dimnames(output$Delta) <- list(group.names, party.names[1:(NP-1)], 1:sample)
if(ret.beta==0){
output$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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