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rhierLinearMixture=function(Data,Prior,Mcmc){
#
# revision history:
# changed 12/17/04 by rossi to fix bug in drawdelta when there is zero/one unit
# in a mixture component
# adapted to linear model by Vicky Chen 6/06
# put in classes 3/07
# changed a check 9/08
# W. Taylor 4/15 - added nprint option to MCMC argument
#
# purpose: run hierarchical linear model with mixture of normals
#
# Arguments:
# Data contains a list of (regdata, and possibly Z)
# regdata is a list of lists (one list per unit)
# regdata[[i]]=list(y,X)
# y is a vector of observations
# X is a length(y) x nvar matrix of values of
# X vars including intercepts
# Z is an nreg x nz matrix of values of variables
# note: Z should NOT contain an intercept
# Prior contains a list of (nu.e,ssq,deltabar,Ad,mubar,Amu,nu,V,ncomp,a)
# ncomp is the number of components in normal mixture
# if elements of Prior (other than ncomp) do not exist, defaults are used
# Mcmc contains a list of (s,c,R,keep,nprint)
#
# Output: as list containing
# taodraw is R/keep x nreg array of error variances for each regression
# Deltadraw R/keep x nz*nvar matrix of draws of Delta, first row is initial value
# betadraw is nreg x nvar x R/keep array of draws of betas
# probdraw is R/keep x ncomp matrix of draws of probs of mixture components
# compdraw is a list of list of lists (length R/keep)
# compdraw[[rep]] is the repth draw of components for mixtures
#
# Priors:
# tau_i ~ nu.e*ssq_i/chisq(nu.e) tau_i is the variance of epsilon_i
# beta_i = delta %*% z[i,] + u_i
# u_i ~ N(mu_ind[i],Sigma_ind[i])
# ind[i] ~multinomial(p)
# p ~ dirichlet (a)
# a: Dirichlet parameters for prior on p
# delta is a k x nz array
# delta= vec(D) ~ N(deltabar,A_d^-1)
# mu_j ~ N(mubar,A_mu^-1(x)Sigma_j)
# Sigma_j ~ IW(nu,V^-1)
# ncomp is number of components
#
# MCMC parameters
# R is number of draws
# keep is thinning parameter, keep every keepth draw
# nprint - print estimated time remaining on every nprint'th draw
#
# check arguments
#
#--------------------------------------------------------------------------------------------------
#
# create functions needed
#
append=function(l) { l=c(l,list(XpX=crossprod(l$X),Xpy=crossprod(l$X,l$y)))}
#
getvar=function(l) {
v=var(l$y)
if(is.na(v)) return(1)
if(v>0) return (v) else return (1)}
#
if(missing(Data)) {pandterm("Requires Data argument -- list of regdata, and (possibly) Z")}
if(is.null(Data$regdata)) {pandterm("Requires Data element regdata (list of data for each unit)")}
regdata=Data$regdata
nreg=length(regdata)
drawdelta=TRUE
if(is.null(Data$Z)) { cat("Z not specified",fill=TRUE); fsh() ; drawdelta=FALSE}
else {if (!is.matrix(Data$Z)) {pandterm("Z must be a matrix")}
else {if (nrow(Data$Z) != nreg) {pandterm(paste("Nrow(Z) ",nrow(Z),"ne number regressions ",nreg))}
else {Z=Data$Z}}}
if(drawdelta) {
nz=ncol(Z)
colmeans=apply(Z,2,mean)
if(sum(colmeans) > .00001)
{pandterm(paste("Z does not appear to be de-meaned: colmeans= ",colmeans))}
}
#
# check regdata for validity
#
for (i in 1:nreg) {
if(!is.matrix(regdata[[i]]$X)) {pandterm(paste0("regdata[[",i,"]]$X must be a matrix"))}
if(!is.vector(regdata[[i]]$y, mode = "numeric") & !is.vector(regdata[[i]]$y, mode = "logical") & !is.matrix(regdata[[i]]$y))
{pandterm(paste0("regdata[[",i,"]]$y must be a numeric or logical vector or matrix"))}
if(is.matrix(regdata[[i]]$y)){ if(ncol(regdata[[i]]$y)>1) {pandterm(paste0("regdata[[",i,"]]$y must be a vector or one-column matrix"))}}
}
dimfun=function(l) {c(length(l$y),dim(l$X))}
dims=sapply(regdata,dimfun)
dims=t(dims)
nvar=quantile(dims[,3],prob=.5)
for (i in 1:nreg)
{
if(dims[i,1] != dims[i,2] || dims[i,3] !=nvar)
{pandterm(paste("Bad Data dimensions for unit ",i," dims(y,X) =",dims[i,]))}
}
#
# check on prior
#
if(missing(Prior))
{pandterm("Requires Prior list argument (at least ncomp)")}
if(is.null(Prior$nu.e)) {nu.e=BayesmConstant.nu.e}
else {nu.e=Prior$nu.e}
if(is.null(Prior$ssq)) {ssq=sapply(regdata,getvar)}
else {ssq=Prior$ssq}
if(is.null(Prior$ncomp)) {pandterm("Requires Prior element ncomp (num of mixture components)")} else {ncomp=Prior$ncomp}
if(is.null(Prior$mubar)) {mubar=matrix(rep(0,nvar),nrow=1)} else { mubar=matrix(Prior$mubar,nrow=1)}
if(ncol(mubar) != nvar) {pandterm(paste("mubar must have ncomp cols, ncol(mubar)= ",ncol(mubar)))}
if(is.null(Prior$Amu)) {Amu=matrix(BayesmConstant.A,ncol=1)} else {Amu=matrix(Prior$Amu,ncol=1)}
if(ncol(Amu) != 1 | nrow(Amu) != 1) {pandterm("Am must be a 1 x 1 array")}
if(is.null(Prior$nu)) {nu=nvar+BayesmConstant.nuInc} else {nu=Prior$nu}
if(nu < 1) {pandterm("invalid nu value")}
if(is.null(Prior$V)) {V=nu*diag(nvar)} else {V=Prior$V}
if(sum(dim(V)==c(nvar,nvar)) !=2) pandterm("Invalid V in prior")
if(is.null(Prior$Ad) & drawdelta) {Ad=BayesmConstant.A*diag(nvar*nz)} else {Ad=Prior$Ad}
if(drawdelta) {if(ncol(Ad) != nvar*nz | nrow(Ad) != nvar*nz) {pandterm("Ad must be nvar*nz x nvar*nz")}}
if(is.null(Prior$deltabar)& drawdelta) {deltabar=rep(0,nz*nvar)} else {deltabar=Prior$deltabar}
if(drawdelta) {if(length(deltabar) != nz*nvar) {pandterm("deltabar must be of length nvar*nz")}}
if(is.null(Prior$a)) { a=rep(BayesmConstant.a,ncomp)} else {a=Prior$a}
if(length(a) != ncomp) {pandterm("Requires dim(a)= ncomp (no of components)")}
bada=FALSE
for(i in 1:ncomp) { if(a[i] < 0) bada=TRUE}
if(bada) pandterm("invalid values in a vector")
#
# check on Mcmc
#
if(missing(Mcmc))
{pandterm("Requires Mcmc list argument")}
else
{
if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep}
if(is.null(Mcmc$R)) {pandterm("Requires R argument in Mcmc list")} else {R=Mcmc$R}
if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint}
if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')}
}
#
# print out problem
#
cat(" ",fill=TRUE)
cat("Starting MCMC Inference for Hierarchical Linear Model:",fill=TRUE)
cat(" Normal Mixture with",ncomp,"components for first stage prior",fill=TRUE)
cat(paste(" for ",nreg," cross-sectional units"),fill=TRUE)
cat(" ",fill=TRUE)
cat("Prior Parms: ",fill=TRUE)
cat("nu.e =",nu.e,fill=TRUE)
cat("nu =",nu,fill=TRUE)
cat("V ",fill=TRUE)
print(V)
cat("mubar ",fill=TRUE)
print(mubar)
cat("Amu ", fill=TRUE)
print(Amu)
cat("a ",fill=TRUE)
print(a)
if(drawdelta)
{
cat("deltabar",fill=TRUE)
print(deltabar)
cat("Ad",fill=TRUE)
print(Ad)
}
cat(" ",fill=TRUE)
cat("MCMC Parms: ",fill=TRUE)
cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE)
cat("",fill=TRUE)
# initialize values
#
# Create XpX elements of regdata and initialize tau
#
regdata=lapply(regdata,append)
tau=sapply(regdata,getvar)
#
# set initial values for the indicators
# ind is of length(nreg) and indicates which mixture component this obs
# belongs to.
#
ind=NULL
ninc=floor(nreg/ncomp)
for (i in 1:(ncomp-1)) {ind=c(ind,rep(i,ninc))}
if(ncomp != 1) {ind = c(ind,rep(ncomp,nreg-length(ind)))} else {ind=rep(1,nreg)}
#
#initialize delta
#
if (drawdelta){
olddelta = rep(0,nz*nvar)
} else { #send placeholders to the _loop function if there is no Z matrix
olddelta = 0
Z = matrix(0)
deltabar = 0
Ad = matrix(0)
}
#
# initialize probs
#
oldprob=rep(1/ncomp,ncomp)
###################################################################
# Wayne Taylor
# 09/19/2014
###################################################################
draws = rhierLinearMixture_rcpp_loop(regdata, Z,
deltabar, Ad, mubar, Amu,
nu, V, nu.e, ssq,
R, keep, nprint, drawdelta,
as.matrix(olddelta), a, oldprob, ind, tau)
####################################################################
attributes(draws$taudraw)$class=c("bayesm.mat","mcmc")
attributes(draws$taudraw)$mcpar=c(1,R,keep)
if(drawdelta){
attributes(draws$Deltadraw)$class=c("bayesm.mat","mcmc")
attributes(draws$Deltadraw)$mcpar=c(1,R,keep)}
attributes(draws$betadraw)$class=c("bayesm.hcoef")
attributes(draws$nmix)$class="bayesm.nmix"
return(draws)
}
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