https://github.com/cran/HLSM
Tip revision: d08047e
helper.R
``````getBeta = function(object, burnin = 0, thin = 1){
if(all(!is.na(object\$draws\$Beta))){
xx = object\$draws\$Beta
nn = dim(xx)[[1]]
dd=seq((burnin+1), nn, thin)
if(length(dim(xx)) == 3){ #for random effect model
return(xx[dd,,]) }
if(length(dim(xx)) == 2){  ##for fixed effect model
return(xx[dd,]) }
}else(return(NULL))
}

getIntercept = function(object, burnin = 0, thin = 1){
xx = object\$draws\$Intercept
if(class(xx) == 'matrix'){  ##for fixed effect model
nn = dim(xx)[[1]]
dd = seq((burnin+1), nn, thin)
return(xx[dd,])
}else{			#for random effect model
nn = length(xx)
dd = seq((burnin+1), nn, thin)
return(xx[dd])
}
}

getAlpha = function(object, burnin = 0, thin = 1){
if(all(!is.na(object\$draws\$Alpha))){
xx = object\$draws\$Alpha
nn = length(xx)
dd = seq((burnin+1), nn, thin)
return(xx[dd])
}else(return(NULL))
}

getLS = function(object, burnin = 0, thin = 1){
xx = object\$draws\$ZZ
nn = length(xx)
dd = seq((burnin+1),nn,thin)
kk = length(xx[[1]])
lp = list()
for(i in 1:kk){
lp.sub = array(0,dim=c(dim(xx[[1]][[i]])[1],dim(xx[[1]][[i]])[2],length(dd)))
for(j in 1:length(dd)){
ind = dd[j]
lp.sub[,,j] = xx[[ind]][[i]]
}
lp[[i]] = lp.sub
}
return(lp)
#	return(sapply(dd,function(w) lapply(1:kk, function(y) xx[[w]][[y]]))
}

getLikelihood = function(object, burnin = 0, thin = 1){
xx = object\$draws\$likelihood
nn = length(xx)
dd = seq(burnin, nn, thin)
return(xx[dd])
}

``````