Nothing
#######################################################
#' Distributed learning for a longitudinal continuous-time zero-inflated Poisson
#' hidden Markov model, where zero-inflation only happens in State 1 with covariates
#' in the state-dependent parameters and transition rates.
#' @param ylist list of observed time series values for each subject
#' @param xlist list of design matrices for each subject.
#' @param timelist list of time indices
#' @param M number of latent states
#' @param initparm matrix of initial working parameters for prior, transition,
#' zero proportion, and emission parameters.
#' @param yceil a scalar defining the ceiling of y, above which the values will be
#' truncated. Default to NULL.
#' @param rho tuning parameter in the distributed learning algorithm. Default
#' to 1.
#' @param priorclust a vector to specify the grouping for state prior. Default to
#' NULL, which means no grouping.
#' @param tpmclust a vector to specify the grouping for the intercepts in state
#' transition rates. Default to NULL, which means no grouping.
#' @param tpmslopeclust a vector to specify the grouping for the slopes in state
#' transition rates. Default to NULL, which means no grouping.
#' @param emitclust a vector to specify the grouping for the intercepts in Poisson
#' regressions. Default to NULL, which means no grouping.
#' @param zeroclust a vector to specify the grouping for the intercepts in ZIP
#' regression. Default to NULL, which means no grouping.
#' @param slopeclust a vector to specify the grouping for the slopes in Poisson and
#' ZIP regressions. Default to NULL, which means no grouping.
#' @param group a list containing group information.
#' @param maxit maximum number iteration. Default to 100.
#' @param tol tolerance in the terms of the relative change in the norm of the
#' common coefficients. Default to 1e-4.
#' @param ncores number of cores to be used for parallel programming. Default to 1.
#' @param seed a seed for the random initialization of the algorithm
#' @param method method for the distributed optimization in the ADMM framework.
#' @param print whether to print each iteration. Default to TRUE.
#' @param libpath path for the ziphsmm library if not the default set up. Default to NULL.
#' @param ... Further arguments passed on to the optimization methods
#' @return the maximum likelihood estimates of the zero-inflated hidden Markov model
#' @references Boyd, S., Parikh, N., Chu, E., Peleato, B. and Eckstein, J., 2011.
#' Distributed optimization and statistical learning via the alternating direction method
#' of multipliers. Foundations and Trends in Machine Learning, 3(1), pp.1-122.
#' @examples
#' \dontrun{
#' set.seed(12933)
#' nsubj <- 10
#' ns <- 2000
#' ylist <- vector(mode="list",length=nsubj)
#' xlist <- vector(mode="list",length=nsubj)
#' timelist <- vector(mode="list",length=nsubj)
#'
#' priorparm <- 0
#' tpmparm <- c(-2,0.1,-2,-0.2)
#' zeroindex <- c(1,0)
#' zeroparm <- c(0,0.5)
#' emitparm <- c(2,0.2,3,0.3)
#' workparm <- NULL
#'
#' for(n in 1:nsubj){
#'
#' xlist[[n]] <- matrix(rep(c(0,1),rep(1000,2)),nrow=2000,ncol=1)
#'
#' timeindex <- rep(1,2000)
#' for(i in 2:2000) timeindex[i] <- timeindex[i-1] + sample(1:4,1)
#' timelist[[n]] <- timeindex
#'
#' workparm <- rbind(workparm,c(priorparm,tpmparm,zeroparm,emitparm))
#'
#' result <- hmmsim3.cont(workparm,2,2000,zeroindex,x=xlist[[n]],timeindex=timeindex)
#' ylist[[n]] <- result$series
#' }
#'
#'
#' ####
#' M <- 2
#' priorclust <- c(rep(1,5),rep(2,5))
#' tpmclust <- c(rep(1,5),rep(2,5))
#' tpmslopeclust <- c(rep(1,5),rep(2,5))
#' zeroclust <- NULL
#' emitclust <- NULL
#' slopeclust <- rep(1,10)
#'
#' group <- vector(mode="list",length=2)
#' group[[1]] <- 1:5; group[[2]] <- 6:10
#' ###
#' time <- proc.time()
#' result <- dist_learn3(ylist, xlist, timelist, 2,workparm,
#' NULL, rho=1, priorclust,tpmclust,tpmslopeclust,
#' emitclust,zeroclust,slopeclust,group,ncores=1,
#' maxit=20, tol=1e-4, method="CG",print=TRUE)
#' proc.time() - time
#' }
#' @useDynLib ziphsmm
#' @importFrom Rcpp evalCpp
#' @export
dist_learn3 <- function(ylist, xlist, timelist, M, initparm, yceil=NULL,
rho=1, priorclust=NULL,tpmclust=NULL,tpmslopeclust=NULL,
emitclust=NULL,zeroclust=NULL,slopeclust=NULL,group,
maxit=100, tol=1e-4, ncores=1,seed=0,
method="Nelder-Mead", print=TRUE, libpath=NULL,...){
nsubj <- length(ylist)
#retrieve working parameters
ncolx <- ncol(xlist[[1]]) + 1
allparm <- initparm
#map [pi,tpmint,tpmslope,zeroint,zerocov,emit1int,emit1cov,...,emitMint,emitMcov]
#to [pi,tpmint,tpmslope,zeroint,emit1int,...,emitMint,zerocov,emit1cov,...,emitMcov]
mapf <- function(oldparm,M,ncolx){
part1 <- oldparm[1:(M-1)]
lid <- (M-1+M*(M-1)*ncolx)
part2 <- oldparm[seq(M,length=M*(M-1),by=ncolx)]
part3 <- oldparm[-c(1:(M-1),
seq(M,length=M*(M-1),by=ncolx),
(lid+1):length(oldparm))]
part4 <- oldparm[seq(lid+1,length=M+1,by=ncolx)]
part5 <- oldparm[-c(1:lid,seq(lid+1,length=M+1,by=ncolx))]
return(c(part1,part2,part3,part4,part5))
}
invmapf <- function(newparm,M,ncolx){
result <- rep(NA, M-1+M*(M-1)*ncolx+ncolx*(1+M))
lid <- (M-1+M*(M-1)*ncolx)
result[1:(M-1)] <- newparm[1:(M-1)]
result[seq(M,length=M*(M-1),by=ncolx)] <- newparm[(M):(M+M*(M-1)-1)]
result[-c(1:(M-1),
seq(M,length=M*(M-1),by=ncolx),
(lid+1):length(result))] <- newparm[(M-1+M*(M-1)+1):(M-1+M*(M-1)+M*(M-1)*(ncolx-1))]
result[seq(lid+1,length=M+1,by=ncolx)] <- newparm[(lid+1):(lid+M+1)]
result[-c(1:lid,seq(lid+1,length=M+1,by=ncolx))] <- newparm[(lid+M+2):length(result)]
return(result)
}
#newparm <- mapf(allparm,M,ncolx)
#invmapf(newparm,M,ncolx)
ntotal <- ncol(allparm)
#########################
#initial J matrix
J <- diag(1,ntotal)
lz <- 0
#cannot be totally subject-specific
totalgroup <- max(c(priorclust,tpmclust,tpmslopeclust,emitclust,zeroclust,slopeclust,1))
commonindex <- NULL #for z
last <- 0
lastcommon <- 0
lastcluster <- 0
clusterindex <- vector(mode="list",length=totalgroup)
last2 <- 0 #for theta
lastcommon2 <- 0
lastcluster2 <- 0
commonindex2 <- NULL
clusterindex2 <- NULL
jcommonindex <- NULL #for constraint id in J
jclusterindex <- NULL
lastjcommon <- 0
lastjcluster <- 0
lastj <- 0
rowtodelete <- NULL
if(is.null(priorclust)){#subject specific
rowtodelete <- c(rowtodelete,1:(M-1))}else if(max(priorclust)==1){#common
lz <- lz + (M-1)*max(priorclust)
commonindex <- c(commonindex,seq(1,length=M-1,by=1))
lastcommon <- max(commonindex)
commonindex2 <- c(commonindex2,1:(M-1))
lastcommon2 <- M-1
jcommonindex <- c(jcommonindex,1:(M-1))
lastjcluster <- M-1
}else{#clustering
lz <- lz + (M-1)*max(priorclust)
for(g in 1:totalgroup) clusterindex[[g]] <- c(clusterindex[[g]],
seq(g,length=M-1,by=totalgroup))
lastcluster <- max(clusterindex[[totalgroup]])
clusterindex2 <- c(clusterindex2,1:(M-1))
lastcluster2 <- M-1
jclusterindex <- c(jclusterindex,1:(M-1))
lastjcluster <- M-1
}
last <- max(c(lastcommon,lastcluster))
last2 <- max(c(lastcommon2,lastcluster2))
lastj <- max(lastjcluster,lastjcommon)
if(is.null(tpmclust)){#subject specific
rowtodelete <- c(rowtodelete,M:(M*M-1))}else if(max(tpmclust)==1){#common
lz <- lz + M*(M-1)*max(tpmclust)
commonindex <- c(commonindex,seq(last+1,length=M*(M-1),by=1))
lastcommon <- max(commonindex)
commonindex2 <- c(commonindex2,M:(M*M-1))
lastcommon2 <- M*M-1
jcommonindex <- c(jcommonindex,(lastj+1):(lastj+M*(M-1)))
lastjcommon <- max(jcommonindex)
}else{#clustering
lz <- lz + M*(M-1)*max(tpmclust)
for(g in 1:totalgroup) clusterindex[[g]] <- c(clusterindex[[g]],
seq(last+g,length=M*(M-1),by=totalgroup))
lastcluster <- max(clusterindex[[totalgroup]])
clusterindex2 <- c(clusterindex2,M:(M*M-1))
lastcluster2 <- M*M-1
jclusterindex <- c(jclusterindex,(lastj+1):(lastj+M*(M-1)))
lastjcluster <- max(jclusterindex)
}
last <- max(c(lastcommon,lastcluster))
last2 <- max(c(lastcommon2,lastcluster2))
lastj <- max(lastjcluster,lastjcommon)
if(is.null(tpmslopeclust)){#subject specific
rowtodelete <- c(rowtodelete,(M*M):(M*M+M*(M-1)*(ncolx-1)-1))}else if(max(tpmslopeclust)==1){#common
lz <- lz + M*(M-1)*max(tpmslopeclust)
commonindex <- c(commonindex,seq(last+1,length=M*(M-1)*(ncolx-1),by=1))
lastcommon <- max(commonindex)
commonindex2 <- c(commonindex2,(M*M):(M*M+M*(M-1)*(ncolx-1)-1))
lastcommon2 <- M*M+M*(M-1)*(ncolx-1)-1
jcommonindex <- c(jcommonindex,(lastj+1):(lastj+M*(M-1)*(ncolx-1)))
lastjcommon <- max(jcommonindex)
}else{#clustering
lz <- lz + M*(M-1)*(ncolx-1)*max(tpmslopeclust)
for(g in 1:totalgroup) clusterindex[[g]] <- c(clusterindex[[g]],
seq(last+g,length=M*(M-1),by=totalgroup))
lastcluster <- max(clusterindex[[totalgroup]])
clusterindex2 <- c(clusterindex2,(M*M):(M*M+M*(M-1)*(ncolx-1)-1))
lastcluster2 <- M*M+M*(M-1)*(ncolx-1)-1
jclusterindex <- c(jclusterindex,(lastj+1):(lastj+M*(M-1)*(ncolx-1)))
lastjcluster <- max(jclusterindex)
}
last <- max(c(lastcommon,lastcluster))
last2 <- max(c(lastcommon2,lastcluster2))
lastj <- max(lastjcluster,lastjcommon)
if(is.null(zeroclust)){#subject specific
rowtodelete <- c(rowtodelete,(M*M+M*(M-1)*(ncolx-1))) }else if(max(zeroclust)==1){#common
lz <- lz + max(zeroclust)
commonindex <- c(commonindex,last+1)
lastcommon <- max(commonindex)
commonindex2 <- c(commonindex2,(M*M+M*(M-1)*(ncolx-1)))
lastcommon2 <- (M*M+M*(M-1)*(ncolx-1))
jcommonindex <- c(jcommonindex,lastj+1)
lastjcommon <- lastj+1
}else{#clustering
lz <- lz + max(zeroclust)
for(g in 1:totalgroup) clusterindex[[g]] <- c(clusterindex[[g]],last+g)
lastcluster <- max(clusterindex[[totalgroup]])
clusterindex2 <- c(clusterindex2,(M*M+M*(M-1)*(ncolx-1)))
lastcluster2 <- (M*M+M*(M-1)*(ncolx-1))
jclusterindex <- c(jclusterindex,lastj+1)
lastjcluster <- lastj+1
}
last <- max(c(lastcommon,lastcluster))
last2 <- max(c(lastcommon2,lastcluster2))
lastj <- max(c(lastjcommon,lastjcluster))
if(is.null(emitclust)){#subject specific
rowtodelete <- c(rowtodelete,(M*M+M*(M-1)*(ncolx-1)+1):(M*M+M*(M-1)*(ncolx-1)+M)) }else if(max(emitclust)==1){#common
lz <- lz + M*max(emitclust)
commonindex <- c(commonindex,seq(last+1,length=M,by=1))
lastcommon <- max(commonindex)
commonindex2 <- c(commonindex2,(M*M+M*(M-1)*(ncolx-1)+1):(M*M+M*(M-1)*(ncolx-1)+M))
lastcommon2 <- max(commonindex2)
jcommonindex <- c(jcommonindex,(lastj+1):(lastj+M))
lastjcommon <- max(jcommonindex)
}else{
lz <- lz + M*max(emitclust)
for(g in 1:totalgroup) clusterindex[[g]] <- c(clusterindex[[g]],
seq(last+g,length=M,by=totalgroup))
lastcluster <- max(clusterindex[[totalgroup]])
clusterindex2 <- c(clusterindex2,(M*M+M*(M-1)*(ncolx-1)+1):(M*M+M*(M-1)*(ncolx-1)+M))
lastcluster2 <- max(clusterindex2)
jclusterindex <- c(jclusterindex,(lastj+1):(lastj+M))
lastjcluster <- max(jclusterindex)
}
last <- max(c(lastcommon,lastcluster))
last2 <- max(c(lastcommon2,lastcluster2))
lastj <- max(c(lastjcommon,lastjcluster))
if(is.null(slopeclust)){#subject specific
rowtodelete <- c(rowtodelete,(M*M+M*(M-1)*(ncolx-1)+M+1):ntotal) }else if(max(slopeclust)==1){#common
lz <- lz + (ncolx-1)*(M+1)*max(slopeclust)
commonindex <- c(commonindex,seq(last+1,length=(ncolx-1)*(M+1),by=1))
lastcommon <- max(commonindex)
commonindex2 <- c(commonindex2,(M*M+M*(M-1)*(ncolx-1)+M+1):ntotal)
lastcommon2 <- max(commonindex2)
jcommonindex <- c(jcommonindex,(lastj+1):(lastj+(ncolx-1)*(M+1)))
lastjcommon <- max(jcommonindex)
}else{
lz <- lz + (ncolx-1)*(M+1)*max(slopeclust)
for(g in 1:totalgroup) clusterindex[[g]] <- c(clusterindex[[g]],
seq(last+g,length=(ncolx-1)*(M+1),by=totalgroup))
lastcluster <- max(clusterindex[[totalgroup]])
clusterindex2 <- c(clusterindex2,(M*M+M*(M-1)*(ncolx-1)+M+1):ntotal)
lastcluster2 <- max(clusterindex2)
jclusterindex <- c(jclusterindex,(lastj+1):(lastj+(ncolx-1)*(M+1)))
lastjcluster <- max(jclusterindex)
}
last <- max(c(lastcommon,lastcluster))
last2 <- max(c(lastcommon2,lastcluster2))
lastj <- max(c(lastjcommon,lastjcluster))
#paramters and their gradients
if(!is.null(rowtodelete)) J <- J[-rowtodelete,]
set.seed(seed)
parm <- t(sapply(1:nrow(allparm), function(gg){
thisrand <- runif(ncol(allparm),-0.01,0.01)
this <- allparm[gg,] + thisrand
mapf(this,M,ncolx)
}))
l <- matrix(0,nsubj,lz)
z <- numeric(lz)
#must have some common effects
#otherwise, just split into subgroups and refit
#must have some common effects
#otherwise, just split into subgroups and refit
if(is.null(commonindex)){
print("Must have some common effects! Otherwise, simply split into clusters and refit.")
}else if (totalgroup == 1){
tempcluster <- vector(mode="list",length=totalgroup)
#initial value
tempcommon <- J[jcommonindex,commonindex2]%*%t(parm[,commonindex2])
z[commonindex] <- rowMeans(tempcommon) + colMeans(l[,commonindex])/rho
olddiff <- sum((tempcommon-z[commonindex])^2)
oldnorm <- sum(z^2)
olddualdiff <- 0
olddualparm <- l
olddualnorm <- 0
zipnegloglik_cov_cont3 <- ziphsmm::zipnegloglik_cov_cont3
newf <- function(initparm,y,covariates,M,ntimes,timeindex,
zi,rho,li){
cov <- cbind(1,covariates)
part1 <- zipnegloglik_cov_cont3(initparm,y,cov,M,ntimes,timeindex)
parmnew <- mapf(initparm,M,ncol(covariates)+1)
diff <- J%*%parmnew - zi
part2 <- t(li)%*%diff
part3 <- 0.5*rho*t(diff)%*%diff
return(part1+part2+part3)
}
newgradf <- function(initparm,y,covariates,M,ntimes,timeindex,
zi,rho,li){
cov <- cbind(1,covariates)
part1 <- pracma::grad(zipnegloglik_cov_cont3,initparm,
y=y,covariates=covariates,M=M,
ntimes=ntimes,timeindex=timeindex)
part2 <- t(J)%*%li
parmnew <- mapf(initparm,M,ncol(covariates)+1)
part3 <- rho * (t(J) %*% ( J%*%parmnew - zi))
return(part1+part2+part3)
}
#start iterations
iteration <- 1
nllk <- 0
dual_change <- NULL
nllk_change <- NULL
primal_change <- NULL
resid_change <- NULL
#recursion
while(iteration<=maxit){
newrho <- rho
#newrho <- rho * iteration^(-1)
#distributed
oldlik <- nllk
if(ncores==1){
#time <- proc.time()
tempresult <- lapply(1:nsubj, function(i){
y <- ylist[[i]]
x <- xlist[[i]]
if(!is.null(yceil)) y <- ifelse(y>yceil, yceil, y)
timeindex <- timelist[[i]]
ntimes <- length(y)
#get subject-specific z and l
for(kk in 1:totalgroup)
if(i%in%group[[kk]]){gi <- kk}else{next}
fullindex <- c(clusterindex[[gi]],commonindex) #for z and l
zi <- z[fullindex]
li <- l[i,fullindex]
initparm <- invmapf(parm[i,],M,ncolx)
optim(par=initparm,fn=newf,gr=newgradf,
M=M,y=y,covariates=x,ntimes=ntimes,
timeindex=timeindex,
zi=zi,rho=newrho,li=li,
method=method,...)
#newf(initparm,y,x,M,ntimes,timeindex,zi,rho,li)
#newgradf(initparm,y,x,M,ntimes,timeindex,zi,rho,li)
})
# proc.time() - time
}else{
cl <- parallel::makeCluster(ncores)
parallel::clusterExport(cl,c("M","ylist","xlist","timelist","yceil","l","parm",
"z","newrho","method","mapf","invmapf","group",
"newf","newgradf","grad_zipnegloglik_nocov_cont",
"zipnegloglik_nocov_cont","J","libpath",
"totalgroup","clusterindex","commonindex"),
envir=environment())
#time <- proc.time()
tempresult <- parallel::parLapply(cl, 1:nsubj, function(i){
if(!is.null(libpath)) .libPaths(libpath) #'~/R_p4/library'
library(ziphsmm)
y <- ylist[[i]]
x <- xlist[[i]]
if(!is.null(yceil)) y <- ifelse(y>yceil, yceil, y)
timeindex <- timelist[[i]]
ntimes <- length(y)
for(kk in 1:totalgroup)
if(i%in%group[[kk]]){gi <- kk}else{next}
fullindex <- c(clusterindex[[gi]],commonindex) #for z and l
zi <- z[fullindex]
li <- l[i,fullindex]
initparm <- invmapf(parm[i,],M,ncolx)
optim(par=initparm,fn=newf,gr=newgradf,
M=M,y=y,covariates=x,ntimes=ntimes,
timeindex=timeindex,
zi=zi,rho=newrho,li=li,
method=method)
})
parallel::stopCluster(cl)
#proc.time()-time
}
#####
nllk <- sum(sapply(1:nsubj,function(i)tempresult[[i]]$value))
parm <- t(sapply(1:nsubj,function(i) {
temppar <- tempresult[[i]]$par
mapf(temppar,M,ncolx)
}))
tempcommon <- J[jcommonindex,commonindex2]%*%t(parm[,commonindex2])
z[commonindex] <- rowMeans(tempcommon) + colMeans(l[,commonindex])/rho
newdiff <- sum((tempcommon-z[commonindex])^2)
newnorm <- sum(z^2)
relchange <- newdiff / (1+olddiff)
#resid <- abs(newdiff-olddiff) / (1+olddiff)
resid <- abs(sqrt(newdiff)-sqrt(olddiff)) / (1+sqrt(olddiff))
resid_change <- c(resid_change, resid)
primal_diff <- abs(sqrt(newnorm) - sqrt(oldnorm))/(1+sqrt(oldnorm))
primal_change <- c(primal_change, primal_diff)
#update l
for(i in 1:nsubj){
fullindex <- commonindex #for z and l
l[i,fullindex] <- l[i,fullindex] + rho *
(J%*%parm[i,]-z[fullindex])
}
newdualparm <- l
newdualnorm <- sum(l^2)
newdualdiff <- sum((newdualparm - olddualparm)^2)
reldualchange <- sqrt(newdualdiff) / (sqrt(olddualnorm) +1)
dual_change <- c(dual_change, reldualchange)
if(iteration<=1) likbase <- nllk
new_nllk_change <- abs(nllk-oldlik)/(1+oldlik)
nllk_change <- c(nllk_change,new_nllk_change)
kkt_cur <- max(primal_diff, new_nllk_change)#newzdiff
if(iteration > maxit |
(iteration>2 & kkt_cur < tol )) {
nllk <- oldlik; break}
if(print==TRUE & iteration>=2){
#cat("iter:",iteration, "; kkt_residual:", kkt_cur,"\n")
cat("iter:",iteration,"; change:",kkt_cur,"\n")
}
olddiff <- newdiff #
olddualdiff <- newdualdiff
olddualparm <- newdualparm
olddualnorm <- newdualnorm
oldnorm <- newnorm
old_nllk_change <- new_nllk_change
iteration <- iteration + 1
}
#reorder back
workingparm <- t(sapply(1:nrow(parm),function(kkk) invmapf(parm[kkk,],M,ncolx)))
return(list(working_parm=workingparm,
change=list(primal=primal_change[-1],
dual=dual_change[-1],
resid=resid_change[-1],
nllk_change=nllk_change[-1]),
nllk=nllk))
#############
}else if(totalgroup>1){ #some clustering some common
#most common case
tempcluster <- vector(mode="list",length=totalgroup)
olddiff <- 0
#initial value
for(g in 1:totalgroup) {
tempcluster[[g]] <-
J[jclusterindex,clusterindex2]%*%t(parm[,clusterindex2])[,group[[g]]]
z[clusterindex[[g]]] <- rowMeans(tempcluster[[g]]) +
colMeans(l[group[[g]],clusterindex[[g]]])/rho
#primal residual
olddiff <- olddiff + sum((tempcluster[[g]]-z[clusterindex[[g]]])^2)
}
tempcommon <- J[jcommonindex,commonindex2]%*%t(parm[,commonindex2])
z[commonindex] <- rowMeans(tempcommon) + colMeans(l[,commonindex])/rho
#primal residual
olddiff <- olddiff + sum((tempcommon-z[commonindex])^2)
oldnorm <- sum(z^2)
olddualdiff <- 0
olddualparm <- l
olddualnorm <- 0
#new functions for penalized negloglik
zipnegloglik_cov_cont3 <- ziphsmm::zipnegloglik_cov_cont3
newf <- function(initparm,y,covariates,M,ntimes,timeindex,
zi,rho,li){
cov <- cbind(1,covariates)
part1 <- zipnegloglik_cov_cont3(initparm,y,cov,M,ntimes,timeindex)
parmnew <- mapf(initparm,M,ncol(covariates)+1)
diff <- J%*%parmnew - zi
part2 <- t(li)%*%diff
part3 <- 0.5*rho*t(diff)%*%diff
return(part1+part2+part3)
}
newgradf <- function(initparm,y,covariates,M,ntimes,timeindex,
zi,rho,li){
cov <- cbind(1,covariates)
part1 <- pracma::grad(zipnegloglik_cov_cont3,initparm,
y=y,covariates=covariates,M=M,
ntimes=ntimes,timeindex=timeindex)
part2 <- t(J)%*%li
parmnew <- mapf(initparm,M,ncol(covariates)+1)
part3 <- rho * (t(J) %*% ( J%*%parmnew - zi))
return(part1+part2+part3)
}
#start iterations
iteration <- 1
nllk <- 0
dual_change <- NULL
nllk_change <- NULL
primal_change <- NULL
resid_change <- NULL
#recursion
while(iteration<=maxit){
newrho <- rho
#newrho <- rho * iteration^(-1)
#distributed
oldlik <- nllk
if(ncores==1){
#time <- proc.time()
tempresult <- lapply(1:nsubj, function(i){
y <- ylist[[i]]
x <- xlist[[i]]
if(!is.null(yceil)) y <- ifelse(y>yceil, yceil, y)
timeindex <- timelist[[i]]
ntimes <- length(y)
#get subject-specific z and l
for(kk in 1:totalgroup)
if(i%in%group[[kk]]){gi <- kk}else{next}
fullindex <- c(clusterindex[[gi]],commonindex) #for z and l
zi <- z[fullindex]
li <- l[i,fullindex]
initparm <- invmapf(parm[i,],M,ncolx)
optim(par=initparm,fn=newf,gr=newgradf,
M=M,y=y,covariates=x,ntimes=ntimes,
timeindex=timeindex,
zi=zi,rho=newrho,li=li,
method=method,...)
#newf(initparm,y,x,M,ntimes,timeindex,zi,rho,li)
#newgradf(initparm,y,x,M,ntimes,timeindex,zi,rho,li)
})
# proc.time() - time
}else{
cl <- parallel::makeCluster(ncores)
parallel::clusterExport(cl,c("M","ylist","xlist","timelist","yceil","l","parm",
"z","newrho","method","mapf","invmapf","group",
"newf","newgradf","grad_zipnegloglik_nocov_cont",
"zipnegloglik_nocov_cont","J","libpath",
"totalgroup","clusterindex","commonindex"),
envir=environment())
#time <- proc.time()
tempresult <- parallel::parLapply(cl, 1:nsubj, function(i){
if(!is.null(libpath)) .libPaths(libpath) #'~/R_p4/library'
library(ziphsmm)
y <- ylist[[i]]
x <- xlist[[i]]
if(!is.null(yceil)) y <- ifelse(y>yceil, yceil, y)
timeindex <- timelist[[i]]
ntimes <- length(y)
for(kk in 1:totalgroup)
if(i%in%group[[kk]]){gi <- kk}else{next}
fullindex <- c(clusterindex[[gi]],commonindex) #for z and l
zi <- z[fullindex]
li <- l[i,fullindex]
initparm <- invmapf(parm[i,],M,ncolx)
optim(par=initparm,fn=newf,gr=newgradf,
M=M,y=y,covariates=x,ntimes=ntimes,
timeindex=timeindex,
zi=zi,rho=newrho,li=li,
method=method)
})
parallel::stopCluster(cl)
#proc.time()-time
}
#############################################################
nllk <- sum(sapply(1:nsubj,function(i)tempresult[[i]]$value))
parm <- t(sapply(1:nsubj,function(i) {
temppar <- tempresult[[i]]$par
mapf(temppar,M,ncolx)
}))
#update z
newdiff <- 0
for(g in 1:totalgroup) {
tempcluster[[g]] <-
J[jclusterindex,clusterindex2]%*%t(parm[,clusterindex2])[,group[[g]]]
z[clusterindex[[g]]] <- rowMeans(tempcluster[[g]]) +
colMeans(l[group[[g]],clusterindex[[g]]])/rho
#primal residual
newdiff <- newdiff + sum((tempcluster[[g]]-z[clusterindex[[g]]])^2)
}
tempcommon <- J[jcommonindex,commonindex2]%*%t(parm[,commonindex2])
z[commonindex] <- rowMeans(tempcommon) + colMeans(l[,commonindex])/rho
newdiff <- newdiff + sum((tempcommon-z[commonindex])^2)
resid <- abs(sqrt(newdiff)-sqrt(olddiff)) / (1+sqrt(olddiff))
resid_change <- c(resid_change, resid)
newnorm <- sum(z^2)
primal_diff <- abs(sqrt(newnorm) - sqrt(oldnorm)) / (1+sqrt(oldnorm))
primal_change <- c(primal_change, primal_diff)
#update l
for(i in 1:nsubj){
for(kk in 1:totalgroup)
if(i%in%group[[kk]]){gi <- kk}else{next}
fullindex <- c(clusterindex[[gi]],commonindex) #for z and l
l[i,fullindex] <- l[i,fullindex] + rho *
(J%*%parm[i,]-z[fullindex])
}
newdualparm <- l
newdualnorm <- sum(l^2)
newdualdiff <- sum((newdualparm - olddualparm)^2)
reldualchange <- sqrt(newdualdiff) / (sqrt(olddualnorm) +1)
dual_change <- c(dual_change, reldualchange)
if(iteration<=1) likbase <- nllk
new_nllk_change <- abs(nllk-oldlik)/(1+oldlik)
nllk_change <- c(nllk_change,new_nllk_change)
kkt_cur <- max(primal_diff, new_nllk_change)#newzdiff
if(iteration > maxit |
(iteration>2 & kkt_cur < tol )) {
nllk <- oldlik; break}
if(print==TRUE & iteration>=2){
#cat("iter:",iteration, "; change:", kkt_cur,"\n")
cat("iter:",iteration,"; change",kkt_cur,"\n")
}
olddiff <- newdiff #
olddualdiff <- newdualdiff
olddualparm <- newdualparm
olddualnorm <- newdualnorm
oldnorm <- newnorm
old_nllk_change <- new_nllk_change
iteration <- iteration + 1
}
#reorder back
workingparm <- t(sapply(1:nrow(parm),function(kkk) invmapf(parm[kkk,],M,ncolx)))
return(list(working_parm=workingparm,
change=list(primal=primal_change[-1],
dual=dual_change[-1],
resid=resid_change[-1],
nllk_change=nllk_change[-1]),
nllk=nllk))
}
}
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