R/fixed0.R

In zoib: Bayesian Inference for Beta Regression and Zero-or-One Inflated Beta Regression

fixed0 <-
function(y, n, xmu.1, p.xmu, xsum.1,p.xsum, x0.1,p.x0, prior1, prec.int,  
         prec.DN,  lambda.L1, lambda.L2, lambda.ARD, link,n.chain, inits, seed)
{
  dataIn <- vector("list",12)  
  dataIn.name <- c("y","xmu.1","p.xmu","xsum.1","p.xsum", "x0.1","p.x0",
                   "n","zero","prior1","hyper","link")
  names(dataIn)<- dataIn.name  
  dataIn[[1]] <- y
  dataIn[[2]] <- as.matrix(xmu.1)
  dataIn[[3]] <- p.xmu
  dataIn[[4]] <- as.matrix(xsum.1)
  dataIn[[5]] <- p.xsum      
  dataIn[[6]] <- as.matrix(x0.1)
  dataIn[[7]] <- p.x0  
  dataIn[[8]] <- n
  dataIn[[9]] <- rep(0,n)     
  dataIn[[10]]<- prior1
  dataIn[[11]]<- as.matrix(cbind(prec.int,prec.DN,lambda.L1,lambda.L2,lambda.ARD))
  dataIn[[12]]<- link
  
  
  if(is.null(seed)){
    init <- function( rngname, rngseed){
      list("tmp1" = rnorm(1,0,0.1),
           "tmp2" = rnorm(1,0,0.1),
           "tmp3" = rnorm(1,0,0.1),
           
           "b.tmp" = matrix(rnorm((p.xmu-1)*4,0,0.1),ncol=4),
           "d.tmp" = matrix(rnorm((p.xsum-1)*4,0,0.1),ncol=4),
           "b0.tmp" = matrix(rnorm((p.x0-1)*4,0,0.1),ncol=4),
           
           "sigmab.L1" = runif((p.xmu-1),0,2), 
           "sigmad.L1" = runif((p.xsum-1),0,2), 
           "sigmab0.L1" = runif((p.x0-1),0,2), 
           
           "taub.ARD" = runif((p.xmu-1),0,2), 
           "taud.ARD" = runif((p.xsum-1),0,2), 
           "taub0.ARD" = runif((p.x0-1),0,2), 
           "taub.L2" = runif(1,0,2), 
           "taud.L2" = runif(1,0,2),
           "taub0.L2" = runif(1,0,2))} 
      inits.internal <- list(init( ));
      if(n.chain >= 2) {
        for(j in 2:n.chain) inits.internal <- c(inits.internal,list(init()))} 
    } else{
      init <- function(rngname, rngseed){
      list("tmp1" = rnorm(1,0,0.1),
           "tmp2" = rnorm(1,0,0.1),
           "tmp3" = rnorm(1,0,0.1),
           
           "b.tmp" = matrix(rnorm((p.xmu-1)*4,0,0.1),ncol=4),
           "d.tmp" = matrix(rnorm((p.xsum-1)*4,0,0.1),ncol=4),
           "b0.tmp" = matrix(rnorm((p.x0-1)*4,0,0.1),ncol=4),
           
           "sigmab.L1" = runif((p.xmu-1),0,2), 
           "sigmad.L1" = runif((p.xsum-1),0,2), 
           "sigmab0.L1" = runif((p.x0-1),0,2), 
           
           "taub.ARD" = runif((p.xmu-1),0,2), 
           "taud.ARD" = runif((p.xsum-1),0,2), 
           "taub0.ARD" = runif((p.x0-1),0,2), 
           "taub.L2" = runif(1,0,2), 
           "taud.L2" = runif(1,0,2),
           "taub0.L2" = runif(1,0,2),
           
           .RNG.name = rngname, 
           .RNG.seed = rngseed)}      
      set.seed(seed[1]); inits.internal <- list(init("base::Super-Duper", seed[1]));
      if(n.chain >= 2) {
        for(j in 2:n.chain){ 
          set.seed(seed[j]); 
          inits.internal <- c(inits.internal,list(init("base::Wichmann-Hill",seed[j])))}}  
    }
    
  if(!is.null(inits)){    
    for(i in 1:n.chain){   
      if(!is.null(inits[[i]]$b)) {
        inits.internal[[i]][[1]] <- inits[[i]]$b[1]
        if(p.xmu>=2) inits.internal[[i]][[4]] <- matrix(rep(inits[[i]]$b[2:p.xmu],4), 
                                           ncol=4, byrow=FALSE)}
      if(!is.null(inits[[i]]$d)) {
        inits.internal[[i]][[2]] <- inits[[i]]$d[1]
        if(p.xsum>=2) inits.internal[[i]][[5]] <- matrix(rep(inits[[i]]$d[2:p.xsum],4), 
                                           ncol=4, byrow=FALSE)}
      if(!is.null(inits[[i]]$b0)) {
        inits.internal[[i]][[3]] <- inits[[i]]$b0[1]
        if(p.x0>=2) inits.internal[[i]][[6]] <- matrix(rep(inits[[i]]$b0[2:p.x0],4), 
                                           ncol=4, byrow=FALSE)}
    }}

  op<- system.file("bugs", "fixed0.bug",package="zoib") 
  model <- jags.model(op,data=dataIn,n.adapt=0,inits=inits.internal,n.chains=n.chain) 
  return(model)
}