# bhpm.cluster
# bhpm: Cluster Analysis wrapper
# R. Carragher
# Date: 29/06/2018
Mi <- new.env()
Mi$Id <- "$Id: bhpm.cluster.1a.hier3.lev0.R,v 1.13 2020/03/31 12:42:23 clb13102 Exp clb13102 $"
bhpm.cluster.1a.indep <- function(cluster.data, sim_type = "SLICE", burnin = 10000, iter = 40000, nchains = 3,
global.sim.params = data.frame(type = c("MH", "SLICE"), param = c("sigma_MH", "w"), value = c(0.2,1),
control = c(0,6)),
sim.params = NULL,
monitor = data.frame(variable = c("theta", "gamma", "mu.gamma", "mu.theta",
"sigma2.theta", "sigma2.gamma",
"mu.theta.0", "mu.gamma.0", "tau2.theta.0", "tau2.gamma.0"),
monitor = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
stringsAsFactors = FALSE),
initial_values = NULL,
hyper_params = list(mu.gamma.0.0 = 0, tau2.gamma.0.0 = 10,
mu.theta.0.0 = 0, tau2.theta.0.0 = 10, alpha.gamma.0.0 = 3, beta.gamma.0.0 = 1, alpha.theta.0.0 = 3,
beta.theta.0.0 = 1, alpha.gamma = 3, beta.gamma = 1, alpha.theta = 3, beta.theta = 1), memory_model = "HIGH")
{
cluster = M_global$CLUSTERdata(Mi, cluster.data, iter, nchains, burnin, initial_values)
if (is.null(cluster)) {
return(NULL)
}
cluster.data = cluster$cluster.data
cntrl.data = cluster$cntrl.data
Mi$sim_type <- sim_type
if (nrow(global.sim.params[global.sim.params$type == sim_type,]) != 1) {
message("Missing simulation parametetrs");
return(NULL)
}
Mi$global.sim.param <- global.sim.params[global.sim.params$type == sim_type,]$value
Mi$global.sim.param_ctrl <- global.sim.params[global.sim.params$type == sim_type,]$control
if (Mi$global.sim.param <= 0) {
message("Invalid simulation parametetr value");
return(NULL)
}
Mi$level = 0
sim.params = M_global$CLUSTER_sim_params1a(Mi, sim.params, sim_type, cluster.data, cntrl.data)
monitor = M_global$CLUSTER_monitor_1a_3(monitor)
# Initialise the hyper-parameters
Mi$mu.gamma.0.0 <- hyper_params$mu.gamma.0.0
Mi$tau2.gamma.0.0 <- hyper_params$tau2.gamma.0.0
Mi$alpha.gamma <- hyper_params$alpha.gamma
Mi$beta.gamma <- hyper_params$beta.gamma
Mi$alpha.gamma.0.0 <- hyper_params$alpha.gamma.0.0
Mi$beta.gamma.0.0 <- hyper_params$beta.gamma.0.0
Mi$mu.theta.0.0 <- hyper_params$mu.theta.0.0
Mi$tau2.theta.0.0 <- hyper_params$tau2.theta.0.0
Mi$alpha.theta <- hyper_params$alpha.theta
Mi$beta.theta <- hyper_params$beta.theta
Mi$alpha.theta.0.0 <- hyper_params$alpha.theta.0.0
Mi$beta.theta.0.0 <- hyper_params$beta.theta.0.0
Ret2 = .Call("bhpm1a_poisson_mc_exec", as.integer(nchains), as.integer(burnin),
as.integer(iter), Mi$sim_type,
memory_model,
as.numeric(Mi$global.sim.param),
as.numeric(Mi$global.sim.param_ctrl),
sim.params,
monitor,
as.integer(Mi$nTreatments),
as.integer(Mi$numClusters), as.integer(Mi$level),
Mi$maxOutcome.Grps, as.integer(Mi$numOutcome.Grp), as.integer(Mi$maxOutcomes),
as.integer(t(Mi$nOutcome)), as.integer(aperm(Mi$x)),
as.integer(aperm(Mi$y)),
as.numeric(aperm(Mi$C)),
as.numeric(aperm(Mi$T)),
as.numeric(aperm(Mi$theta)),
as.numeric(aperm(Mi$gamma)),
as.numeric(Mi$mu.gamma.0.0),
as.numeric(Mi$tau2.gamma.0.0),
as.numeric(Mi$mu.theta.0.0),
as.numeric(Mi$tau2.theta.0.0),
as.numeric(Mi$alpha.gamma.0.0),
as.numeric(Mi$beta.gamma.0.0),
as.numeric(Mi$alpha.theta.0.0),
as.numeric(Mi$beta.theta.0.0),
as.numeric(Mi$alpha.gamma),
as.numeric(Mi$beta.gamma),
as.numeric(Mi$alpha.theta),
as.numeric(Mi$beta.theta),
as.numeric(aperm(Mi$mu.gamma.0)),
as.numeric(aperm(Mi$tau2.gamma.0)),
as.numeric(aperm(Mi$mu.theta.0)),
as.numeric(aperm(Mi$tau2.theta.0)),
as.numeric(aperm(Mi$mu.gamma)),
as.numeric(aperm(Mi$mu.theta)),
as.numeric(aperm(Mi$sigma2.gamma)),
as.numeric(aperm(Mi$sigma2.theta)))
mu.gamma.0_samples = NULL
if (monitor[monitor$variable == "mu.gamma.0", ]$monitor == 1) {
mu.gamma.0_samples <- .Call("getMuGamma0SamplesClusterAll")
mu.gamma.0_samples = aperm(mu.gamma.0_samples)
}
mu.theta.0_samples = NULL
if (monitor[monitor$variable == "mu.theta.0", ]$monitor == 1) {
mu.theta.0_samples <- .Call("getMuTheta0SamplesClusterAll")
mu.theta.0_samples = aperm(mu.theta.0_samples)
}
tau2.gamma.0_samples = NULL
if (monitor[monitor$variable == "tau2.gamma.0", ]$monitor == 1) {
tau2.gamma.0_samples <- .Call("getTau2Gamma0SamplesClusterAll")
tau2.gamma.0_samples = aperm(tau2.gamma.0_samples)
}
tau2.theta.0_samples = NULL
if (monitor[monitor$variable == "tau2.theta.0", ]$monitor == 1) {
tau2.theta.0_samples <- .Call("getTau2Theta0SamplesClusterAll")
tau2.theta.0_samples = aperm(tau2.theta.0_samples)
}
mu.theta_samples = NULL
if (monitor[monitor$variable == "mu.theta", ]$monitor == 1) {
mu.theta_samples <- .Call("getMuThetaSamplesClusterAll")
mu.theta_samples <- aperm(mu.theta_samples)
}
mu.gamma_samples = NULL
if (monitor[monitor$variable == "mu.gamma", ]$monitor == 1) {
mu.gamma_samples <- .Call("getMuGammaSamplesClusterAll")
mu.gamma_samples <- aperm(mu.gamma_samples)
}
sigma2.theta_samples = NULL
if (monitor[monitor$variable == "sigma2.theta", ]$monitor == 1) {
sigma2.theta_samples <- .Call("getSigma2ThetaSamplesClusterAll")
sigma2.theta_samples <- aperm(sigma2.theta_samples)
}
sigma2.gamma_samples = NULL
if (monitor[monitor$variable == "sigma2.gamma", ]$monitor == 1) {
sigma2.gamma_samples <- .Call("getSigma2GammaSamplesClusterAll")
sigma2.gamma_samples <- aperm(sigma2.gamma_samples)
}
gamma_samples = NULL
gamma_acc = NULL
if (monitor[monitor$variable == "gamma", ]$monitor == 1) {
gamma_samples = .Call("getGammaSamplesClusterAll")
gamma_samples = aperm(gamma_samples)
gamma_acc = .Call("getGammaAcceptClusterAll")
gamma_acc <- aperm(gamma_acc)
}
theta_samples = NULL
theta_acc = NULL
if (monitor[monitor$variable == "theta", ]$monitor == 1) {
theta_samples = .Call("getThetaSamplesClusterAll")
theta_samples = aperm(theta_samples)
theta_acc = .Call("getThetaAcceptClusterAll")
theta_acc <- aperm(theta_acc)
}
.C("Release_Cluster")
model_fit = list(id = Mi$Id, sim_type = Mi$sim_type, chains = nchains, nClusters = Mi$numClusters,
nTreatments = Mi$nTreatments,
Clusters = Mi$Clusters, Trt.Grps = Mi$Trt.Grps, nOutcome.Grp = Mi$numOutcome.Grp, maxOutcome.Grps = Mi$maxOutcome.Grps,
maxOutcomes = Mi$maxOutcomes, nOutcome = Mi$nOutcome, Outcome=Mi$Outcome, Outcome.Grp = Mi$Outcome.Grp,
burnin = burnin, iter = iter,
monitor = monitor,
gamma = gamma_samples,
theta = theta_samples,
mu.gamma = mu.gamma_samples,
mu.theta = mu.theta_samples,
sigma2.gamma = sigma2.gamma_samples,
sigma2.theta = sigma2.theta_samples,
mu.gamma.0 = mu.gamma.0_samples,
mu.theta.0 = mu.theta.0_samples,
tau2.gamma.0 = tau2.gamma.0_samples,
tau2.theta.0 = tau2.theta.0_samples,
gamma_acc = gamma_acc,
theta_acc = theta_acc)
# Model is poisson with BB1a hierarchy and independent clusters
attr(model_fit, "model") = "1a_pois_indep"
return(model_fit)
}
Mi$initVars = function() {
# Data Structure
Mi$Outcome.Grp <- c()
Mi$numOutcome.Grp <- NA
Mi$numClusters <- NA
Mi$nOutcome <- c()
Mi$maxOutcomes <- NA
# Cluster Event Data
Mi$x <- array()
Mi$C <- array()
Mi$y <- array()
Mi$T <- array()
# Hyperparameters
Mi$mu.gamma.0.0 <- NA
Mi$tau2.gamma.0.0 <- NA
Mi$mu.theta.0.0 <- NA
Mi$tau2.theta.0.0 <- NA
Mi$alpha.gamma.0.0 <- NA
Mi$beta.gamma.0.0 <- NA
Mi$alpha.theta.0.0 <- NA
Mi$beta.theta.0.0 <- NA
Mi$alpha.gamma <- NA
Mi$beta.gamma <- NA
Mi$alpha.theta <- NA
Mi$beta.theta <- NA
# Parameters/Simulated values
# Stage 3
Mi$mu.gamma.0 <- c()
Mi$tau2.gamma.0 <- c()
Mi$mu.theta.0 <- c()
Mi$tau2.theta.0 <- c()
# Stage 2
Mi$mu.gamma <- array()
Mi$mu.theta <- array()
Mi$sigma2.gamma <- array()
Mi$sigma2.theta <- array()
# Stage 1
Mi$theta <- array()
Mi$gamma <- array()
}
Mi$initChains = function(c) {
# Choose random values for gamma and theta
for (i in 1:Mi$numClusters) {
numOutcome.Grp = Mi$numOutcome.Grp[i]
for (b in 1:numOutcome.Grp) {
Mi$gamma[c, i, b, 1:Mi$nOutcome[i, b]] <- runif(Mi$nOutcome[i, b], -10, 10)
Mi$gamma[c, i, b, ][is.infinite(Mi$gamma[c, i, b, ])] = -10
Mi$gamma[c, i, b, ][is.nan(Mi$gamma[c, i, b, ])] = -10 # -1000
for (t in 1:(Mi$nTreatments -1)) {
Mi$theta[c, t, i, b, 1:Mi$nOutcome[i, b]] <- runif(Mi$nOutcome[i, b], -10, 10)
Mi$theta[c, t, i, b, ][is.infinite(Mi$theta[c, t, i, b, ])] = -10
Mi$theta[c, t, i, b, ][is.nan(Mi$theta[c, t, i, b, ])] = -10 # -1000
}
}
Mi$mu.gamma[c, i, 1:numOutcome.Grp] = runif(numOutcome.Grp, -10, 10)
Mi$mu.theta[c,, i, 1:numOutcome.Grp] = runif(numOutcome.Grp*(Mi$nTreatments -1), -10, 10)
Mi$sigma2.gamma[c, i, 1:numOutcome.Grp] = runif(numOutcome.Grp, 5, 20)
Mi$sigma2.theta[c,, i, 1:numOutcome.Grp] = runif(numOutcome.Grp*(Mi$nTreatments -1), 5, 20)
Mi$mu.gamma.0[c, i] = runif(1, -10, 10)
Mi$tau2.gamma.0[c, i] = runif(1, 5, 20)
Mi$mu.theta.0[c,, i] = runif(1*(Mi$nTreatments -1), -10, 10)
Mi$tau2.theta.0[c,, i] = runif(1*(Mi$nTreatments -1), 5, 20)
}
}
Mi$initialiseChains = function(initial_values, nchains) {
Mi$theta = array(0, dim=c(nchains, Mi$nTreatments - 1, Mi$numClusters, Mi$maxOutcome.Grps, Mi$maxOutcomes))
Mi$gamma = array(0, dim=c(nchains, Mi$numClusters, Mi$maxOutcome.Grps, Mi$maxOutcomes))
if (is.null(initial_values)) {
# Initialise the first chain with the data
for (i in 1:Mi$numClusters) {
numOutcome.Grp = Mi$numOutcome.Grp[i]
for (b in 1:numOutcome.Grp) {
Mi$gamma[1, i, b, ] <- log(Mi$x[i, b,]/Mi$C[i, b, ])
for (t in 1:(Mi$nTreatments - 1)) {
Mi$theta[1, t, i, b, ] <- log(Mi$y[t, i, b,]/Mi$T[t, i, b, ]) - Mi$gamma[1, i, b, ]
Mi$theta[1, t, i, b, ][is.infinite(Mi$theta[1, t, i, b, ])] = -10 # -1000
Mi$theta[1, t, i, b, ][is.nan(Mi$theta[1, t, i, b, ])] = -10 # -1000
}
Mi$gamma[1, i, b, ][is.infinite(Mi$gamma[1, i, b, ])] = -10 # -1000
Mi$gamma[1, i, b, ][is.nan(Mi$gamma[1, i, b, ])] = -10 # -1000
}
}
Mi$mu.gamma <- array(0, dim = c(nchains, Mi$numClusters, Mi$maxOutcome.Grps))
Mi$mu.theta <- array(0, dim = c(nchains, Mi$nTreatments - 1, Mi$numClusters, Mi$maxOutcome.Grps))
Mi$sigma2.gamma <- array(10, dim = c(nchains, Mi$numClusters, Mi$maxOutcome.Grps))
Mi$sigma2.theta <- array(10, dim = c(nchains, Mi$nTreatments - 1, Mi$numClusters, Mi$maxOutcome.Grps))
Mi$mu.gamma.0 <- array(0, dim = c(nchains, Mi$numClusters))
Mi$tau2.gamma.0 <- array(10, dim = c(nchains, Mi$numClusters))
Mi$mu.theta.0 <- array(0, dim = c(nchains, Mi$nTreatments - 1, Mi$numClusters))
Mi$tau2.theta.0 <- array(10, dim = c(nchains, Mi$nTreatments - 1, Mi$numClusters))
if (nchains > 1) {
for (c in 2:nchains) {
Mi$initChains(c)
}
}
}
else {
Mi$mu.gamma.0 <- array(0, dim = c(nchains, Mi$numClusters))
Mi$tau2.gamma.0 <- array(10, dim = c(nchains, Mi$numClusters))
Mi$mu.theta.0 <- array(0, dim = c(nchains, Mi$nTreatments - 1, Mi$numClusters))
Mi$tau2.theta.0 <- array(10, dim = c(nchains, Mi$nTreatments - 1, Mi$numClusters))
for (c in 1:nchains) {
for (i in 1:Mi$numClusters) {
cluster = Mi$Clusters[i]
data = initial_values$mu.gamma.0[initial_values$mu.gamma.0$chain == c &
initial_values$mu.gamma.0$Cluster == cluster, ]
Mi$mu.gamma.0[c, i] = data$value
data = initial_values$tau2.gamma.0[initial_values$tau2.gamma.0$chain == c &
initial_values$tau2.gamma.0$Cluster == cluster, ]
Mi$tau2.gamma.0[c, i] = data$value
for (t in 1:(Mi$nTreatments - 1)) {
data = initial_values$mu.theta.0[[t]][initial_values$mu.theta.0[[t]]$chain == c &
initial_values$mu.theta.0[[t]]$Cluster == cluster, ]
Mi$mu.theta.0[c, t, i] = data$value
data = initial_values$tau2.theta.0[[t]][initial_values$tau2.theta.0[[t]]$chain == c &
initial_values$tau2.theta.0[[t]]$Cluster == cluster, ]
Mi$tau2.theta.0[c, t, i] = data$value
}
}
}
Mi$mu.gamma <- array(0, dim = c(nchains, Mi$numClusters, Mi$maxOutcome.Grps))
Mi$sigma2.gamma <- array(0, dim = c(nchains, Mi$numClusters, Mi$maxOutcome.Grps))
Mi$mu.theta <- array(0, dim = c(nchains, Mi$nTreatments - 1, Mi$numClusters, Mi$maxOutcome.Grps))
Mi$sigma2.theta <- array(0, dim = c(nchains, Mi$nTreatments - 1, Mi$numClusters, Mi$maxOutcome.Grps))
for (c in 1:nchains) {
for (i in 1:Mi$numClusters) {
cluster = Mi$Clusters[i]
for (b in 1:Mi$numOutcome.Grp[i]) {
data = initial_values$mu.gamma[initial_values$mu.gamma$chain == c &
initial_values$mu.gamma$Cluster == cluster
& initial_values$mu.gamma$Outcome.Grp == Mi$Outcome.Grp[i, b],]
Mi$mu.gamma[c, i, b] = data$value
data = initial_values$sigma2.gamma[initial_values$sigma2.gamma$chain == c &
initial_values$sigma2.gamma$Cluster == cluster
& initial_values$sigma2.gamma$Outcome.Grp == Mi$Outcome.Grp[i, b],]
Mi$sigma2.gamma[c, i, b] = data$value
for (t in 1:(Mi$nTreatments - 1)) {
data = initial_values$mu.theta[[t]][initial_values$mu.theta[[t]]$chain == c &
initial_values$mu.theta[[t]]$Cluster == cluster
& initial_values$mu.theta[[t]]$Outcome.Grp == Mi$Outcome.Grp[i, b],]
Mi$mu.theta[c, t, i, b] = data$value
data = initial_values$sigma2.theta[[t]][initial_values$sigma2.theta[[t]]$chain == c &
initial_values$sigma2.theta[[t]]$Cluster == cluster
& initial_values$sigma2.theta[[t]]$Outcome.Grp == Mi$Outcome.Grp[i, b],]
Mi$sigma2.theta[c, t, i, b] = data$value
}
}
}
}
for (c in 1:nchains) {
for (i in 1:Mi$numClusters) {
cluster = Mi$Clusters[i]
for (b in 1:Mi$numOutcome.Grp[i]) {
for (j in 1:Mi$nOutcome[i, b]) {
ae = Mi$Outcome[i, b, j]
data = initial_values$gamma[initial_values$gamma$chain == c
& initial_values$gamma$Cluster == cluster
& initial_values$gamma$Outcome.Grp == Mi$Outcome.Grp[i, b]
& initial_values$gamma$Outcome == ae,]
Mi$gamma[c, i, b, j] = data$value
for (t in 1:(Mi$nTreatments - 1)) {
data = initial_values$theta[[t]][initial_values$theta[[t]]$chain == c
& initial_values$theta[[t]]$Cluster == cluster
& initial_values$theta[[t]]$Outcome.Grp == Mi$Outcome.Grp[i, b]
& initial_values$theta[[t]]$Outcome == ae,]
Mi$theta[c, t, i, b, j] = data$value
}
}
}
}
}
}
}
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