R/joint.2z0.R

Defines functions joint.2z0

Documented in joint.2z0

joint.2z0 <-
function(y, n, q, xmu.1, p.xmu, xsum.1, p.xsum, x0.1, p.x0,
                      inflate0, zdummy, qz,nz0, m, rid, EUID, nEU,
                      prior1, prior2, prior.beta, prior.Sigma, 
                      prec.int, prec.DN, lambda.L1, lambda.L2,lambda.ARD,
                      scale.unif, scale.halft, link, n.chain, inits, seed) 
{ 
  dataIn <- vector("list",24)
  dataIn.name <- c("y","n","q","xmu.1","p.xmu", 
                   "xsum.1","p.xsum","x0.1","p.x0","inflate0",
                   "z","nz0","qz","m","zero",
                   "cumm","link","hyper","prior1","prior2",
                   "rid","EUID","nEU","hyper2")
  names(dataIn)<- dataIn.name  
  dataIn[[1]] <- as.matrix(y)
  dataIn[[2]] <- n      
  dataIn[[3]] <- q
  dataIn[[4]] <- as.matrix(xmu.1)
  dataIn[[5]] <- p.xmu
  dataIn[[6]] <- as.matrix(xsum.1) 
  dataIn[[7]] <- p.xsum      
  dataIn[[8]] <- as.matrix(x0.1)
  dataIn[[9]] <- p.x0  
  dataIn[[10]]<- inflate0
  dataIn[[11]]<- zdummy
  dataIn[[12]]<- nz0
  dataIn[[13]]<- qz
  dataIn[[14]]<- m
  dataIn[[15]]<- matrix(0,n,q)  
  dataIn[[16]]<- c(0,cumsum(m[-nz0]))  
  dataIn[[17]]<- link
  dataIn[[18]]<- abind(prec.int,prec.DN,lambda.L1,lambda.L2,lambda.ARD,along=3)    
  dataIn[[19]] <- prior1
  dataIn[[20]] <- prior2    
  dataIn[[21]] <- rid 
  dataIn[[22]] <- EUID
  dataIn[[23]] <- nEU
  if(grepl("unif", prior.Sigma)) dataIn[[24]] <- scale.unif
  if(grepl("halfcauchy",prior.Sigma)) dataIn[[24]] <- scale.halft   
  
  if(is.null(seed)){
    init <- function(rngname, rngseed ){
      rho1 <- runif(1,-0.5,0.5)
      rho2 <- runif(1,-0.5,0.5) 
      rho3 <- runif(1, rho1*rho2 - sqrt((1-rho1^2)*(1-rho2^2)), 
                    rho1*rho2 + sqrt((1-rho1^2)*(1-rho2^2)))
      return(
        list("tmp1" = rnorm(q,0,0.1),
             "tmp2" = rnorm(q,0,0.1),
             "tmp3" = rnorm(q,0,0.1),
             
             "b.tmp" = array(rnorm((p.xmu-1)*4*q,0,0.1), c((p.xmu-1),q,4)),
             "d.tmp" = array(rnorm((p.xsum-1)*4*q,0,0.1),c((p.xsum-1),q,4)),
             "b0.tmp"= array(rnorm((p.x0-1)*4*q,0,0.1),  c((p.x0-1),q,4)),
             
             "sigmab.L1" =  matrix(runif((p.xmu-1)*q,0,2),(p.xmu-1),q), 
             "sigmad.L1" =  matrix(runif((p.xsum-1)*q,0,2),(p.xsum-1),q),  
             "sigmab0.L1" = matrix(runif((p.x0-1)*q,0,2),(p.x0-1),q),  
             
             "taub.ARD" =  matrix(runif((p.xmu-1)*q,0,2), (p.xmu-1),q), 
             "taud.ARD" =  matrix(runif((p.xsum-1)*q,0,2),(p.xsum-1),q),  
             "taub0.ARD" = matrix(runif((p.x0-1)*q,0 ,2),(p.x0-1),q),  
             
             "taub.L2" =  runif(q,0,2), 
             "taud.L2" =  runif(q,0,2),
             "taub0.L2" = runif(q,0,2),
             
             "sigma.VC1" = runif(nz0,0.25,2),
             "t" = runif(nz0,0.25,1),         
             "scale1" = runif(qz,0.25,2),
             "scale2" = runif(qz,0.25,2),
             
             "rho1" = rho1,
             "rho2" = rho2,
             "rho3" = rho3))}
      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 ){
    rho1 <- runif(1,-0.5,0.5)
    rho2 <- runif(1,-0.5,0.5) 
    rho3 <- runif(1, rho1*rho2 - sqrt((1-rho1^2)*(1-rho2^2)), 
                  rho1*rho2 + sqrt((1-rho1^2)*(1-rho2^2)))
    return(
      list("tmp1" = rnorm(q,0,0.1),
           "tmp2" = rnorm(q,0,0.1),
           "tmp3" = rnorm(q,0,0.1),
           
           "b.tmp" = array(rnorm((p.xmu-1)*4*q,0,0.1), c((p.xmu-1),q,4)),
           "d.tmp" = array(rnorm((p.xsum-1)*4*q,0,0.1),c((p.xsum-1),q,4)),
           "b0.tmp"= array(rnorm((p.x0-1)*4*q,0,0.1),  c((p.x0-1),q,4)),
           
           "sigmab.L1" =  matrix(runif((p.xmu-1)*q,0,2),(p.xmu-1),q), 
           "sigmad.L1" =  matrix(runif((p.xsum-1)*q,0,2),(p.xsum-1),q),  
           "sigmab0.L1" = matrix(runif((p.x0-1)*q,0,2),(p.x0-1),q),  
           
           "taub.ARD" =  matrix(runif((p.xmu-1)*q,0,2), (p.xmu-1),q), 
           "taud.ARD" =  matrix(runif((p.xsum-1)*q,0,2),(p.xsum-1),q),  
           "taub0.ARD" = matrix(runif((p.x0-1)*q,0 ,2),(p.x0-1),q),  
           
           "taub.L2" =  runif(q,0,2), 
           "taud.L2" =  runif(q,0,2),
           "taub0.L2" = runif(q,0,2),
           
           "sigma.VC1" = runif(nz0,0.25,2),
           "t" = runif(nz0,0.25,1),         
           "scale1" = runif(qz,0.25,2),
           "scale2" = runif(qz,0.25,2),
           
           "rho1" = rho1,
           "rho2" = rho2,
           "rho3" = rho3,
           
           .RNG.name = rngname, 
           .RNG.seed = rngseed))}  
  
  # 1b, 2d, 3b0, 4d1, 
  # 5 SigmaVC (sigma.VC1 or t),SigmaUN (scale1 or scale2),
  # 6 rho1,2,3
  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]] <- array(rep(inits[[i]]$b[2:p.xmu,],4), c((p.xmu-1),q,4))}
    if(!is.null(inits[[i]]$d)) {
      inits.internal[[i]][[2]] <- inits[[i]]$d[1,]
      if(p.xsum>=2) inits.internal[[i]][[5]] <- array(rep(inits[[i]]$d[2:p.xsum,],4), c((p.xsum-1),q,4))}
    if(!is.null(inits[[i]]$b0)) {
      inits.internal[[i]][[3]] <- inits[[i]]$b0[1,]
      if(p.x0>=2) inits.internal[[i]][[6]] <- array(rep(inits[[i]]$b0[2:p.x0,],4), c((p.x0-1),q,4))}
    
    if(!is.null(inits[[i]]$sigma)) {
      inits.internal[[i]][[16]]<- inits[[i]]$sigma
      inits.internal[[i]][[17]]<- inits[[i]]$sigma
      inits.internal[[i]][[18]]<- runif(qz,0.25,2)
      inits.internal[[i]][[19]]<- runif(qz,0.25,2)
    }
    
    # check PD of the initial R matrix
    if(!is.null(inits[[i]]$R)) {
      notuse <-FALSE
      Rele <- inits[[i]]$R
      size <- (sqrt(1+8*length(Rele))-1)/2 # (# of random effects)
      R <- diag(size)
      R[upper.tri(R, diag=TRUE)] <- Rele 
      R <- R + t(R) - diag(diag(R))
      pd <- all(eigen(R)$values>0)
      if(!pd) {
        notuse <- TRUE
        warning('the specified initial correlation matrix is not positive definite')
        warning('Internal initial value are used')
        break}
      else{
        if(size==2) inits.internal[[i]][[20]] <-inits[[i]]$R[2]
        if(size==3){
          inits.internal[[i]][[20]] <-inits[[i]]$R[2]; 
          inits.internal[[i]][[21]] <-inits[[i]]$R[4]; 
          inits.internal[[i]][[22]] <-inits[[i]]$R[5]}
      }
      lower <- inits.internal[[i]][[20]]*inits.internal[[i]][[21]]-
        sqrt((1-inits.internal[[i]][[20]]^2)*(1-inits.internal[[i]][[21]]^2))
      upper <- inits.internal[[i]][[20]]*inits.internal[[i]][[21]]+
        sqrt((1-inits.internal[[i]][[20]]^2)*(1-inits.internal[[i]][[21]]^2))
      if(inits.internal[[i]][[22]]<lower | inits.internal[[i]][[22]]>upper)
        inits.internal[[i]][[22]] <- runif(1, lower, upper)
    }
  }}
  op<- system.file("bugs", "joint_2z0.bug", package="zoib") 
 
  model <- jags.model(op, data = dataIn, n.adapt=0, inits=inits.internal, n.chains=n.chain)   
  return(model)
}

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zoib documentation built on May 31, 2023, 7:49 p.m.