R/bhpm.cluster.1a.hier2.lev0.convergence.R
In rcarragh/bhpm: Bayesian Hierarchical Poisson Models for Multiple Grouped Outcomes with Clustering
Defines functions
bhpm.cluster.1a.hier2.lev0.print.convergence.summary
bhpm.cluster.1a.hier2.lev0.convergence.diag
# bhpm.cluster.1a.hier2.lev0.convergence.diag
# R. Carragher
# Date: 29/06/2018
#
# If the MCMC simulation has been run for more than one chain report the Gelman-Rubin statistic.
# If the MCMC simulation has been run for only one chain report the Geweke diagnostic (Z-score)
#
Id <- "$Id: bhpm.cluster.1a.hier2.lev0.convergence.R,v 1.9 2020/03/31 12:42:23 clb13102 Exp clb13102 $"
bhpm.cluster.1a.hier2.lev0.convergence.diag <- function(raw, debug_diagnostic = FALSE)
{
if (is.null(raw)) {
message("NULL raw data")
return(NULL)
}
if (M_global$CLUSTER_check_conv_name_1a_2(raw)) {
message("Missing names");
return(NULL)
}
model = attr(raw, "model")
if (is.null(model)) {
message("Simulation model attribute missing")
return(NULL)
}
# Check which variables we are monitoring
monitor = raw$monitor
theta_mon = monitor[monitor$variable == "theta",]$monitor
gamma_mon = monitor[monitor$variable == "gamma",]$monitor
mu.theta_mon = monitor[monitor$variable == "mu.theta",]$monitor
mu.gamma_mon = monitor[monitor$variable == "mu.gamma",]$monitor
sigma2.theta_mon = monitor[monitor$variable == "sigma2.theta",]$monitor
sigma2.gamma_mon = monitor[monitor$variable == "sigma2.gamma",]$monitor
theta.trt.grps <- raw$Trt.Grps[ raw$Trt.Grps$param == "theta", ]$Trt.Grp
nchains = raw$chains
gamma_conv = data.frame(Cluster = character(0), Outcome.Grp = character(0),
Outcome = character(0), stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
theta_conv = data.frame(Trt.Grp = integer(0), Cluster = character(0), Outcome.Grp = character(0), Outcome = character(0),
stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
mu.gamma_conv = data.frame(Cluster = character(0), Outcome.Grp = character(0),
stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
mu.theta_conv = data.frame(Trt.Grp = integer(0), Cluster = character(0), Outcome.Grp = character(0),
stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
sigma2.gamma_conv = data.frame(Cluster = character(0), Outcome.Grp = character(0),
stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
sigma2.theta_conv = data.frame(Trt.Grp = integer(0), Cluster = character(0), Outcome.Grp = character(0),
stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
type <- NA
if (nchains > 1) {
# Gelman-Rubin Statistics
type = "Gelman-Rubin"
for (i in 1:raw$nClusters) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (j in 1:raw$nOutcome[i, b]) {
# theta
for (t in 1:(raw$nTreatments - 1)) {
if (theta_mon == 1) {
g = M_global$GelmanRubin(raw$theta[, t, i, b, j, ], nchains)
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b,j], stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
theta_conv = rbind(theta_conv, row)
}
}
# gamma
if (gamma_mon == 1) {
g = M_global$GelmanRubin(raw$gamma[, i, b, j, ], nchains)
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b,j], stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
gamma_conv = rbind(gamma_conv, row)
}
}
# mu.gamma
if (mu.gamma_mon == 1) {
g = M_global$GelmanRubin(raw$mu.gamma[, i, b, ], nchains)
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
mu.gamma_conv = rbind(mu.gamma_conv, row)
}
# mu.theta
if (mu.theta_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$GelmanRubin(raw$mu.theta[, t, i, b, ], nchains)
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
mu.theta_conv = rbind(mu.theta_conv, row)
}
}
# sigma2.theta
if (sigma2.theta_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$GelmanRubin(raw$sigma2.theta[, t, i, b, ], nchains)
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
sigma2.theta_conv = rbind(sigma2.theta_conv, row)
}
}
# sigma2.gamma
if (sigma2.gamma_mon == 1) {
g = M_global$GelmanRubin(raw$sigma2.gamma[, i, b, ], nchains)
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
sigma2.gamma_conv = rbind(sigma2.gamma_conv, row)
}
}
}
}
else {
# Geweke Diagnostic
type = "Geweke"
for (i in 1:raw$nClusters) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (j in 1:raw$nOutcome[i, b]) {
# theta
if (theta_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$Geweke(raw$theta[1, t, i, b, j, ])
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b, j], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
theta_conv = rbind(theta_conv, row)
}
}
# gamma
if (gamma_mon == 1) {
g = M_global$Geweke(raw$gamma[1, i, b, j, ])
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b, j], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
gamma_conv = rbind(gamma_conv, row)
}
}
# mu.gamma
if (mu.gamma_mon == 1) {
g = M_global$Geweke(raw$mu.gamma[1, i, b, ])
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
mu.gamma_conv = rbind(mu.gamma_conv, row)
}
# mu.theta
if (mu.theta_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$Geweke(raw$mu.theta[1, t, i, b, ])
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
mu.theta_conv = rbind(mu.theta_conv, row)
}
}
# sigma2.theta
if (sigma2.theta_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$Geweke(raw$sigma2.theta[1, t, i, b, ])
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
sigma2.theta_conv = rbind(sigma2.theta_conv, row)
}
}
# sigma2.gamma
if (sigma2.gamma_mon == 1) {
g = M_global$Geweke(raw$sigma2.gamma[1, i, b, ])
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
sigma2.gamma_conv = rbind(sigma2.gamma_conv, row)
}
}
}
}
theta_acc = data.frame(chain = numeric(0), Trt.Grp = integer(0), Cluster = character(0), Outcome.Grp = character(0),
Outcome = character(0), rate = numeric(0), stringsAsFactors=FALSE)
gamma_acc = data.frame(chain = numeric(0), Cluster = character(0), Outcome.Grp = character(0),
Outcome = character(0), rate = numeric(0), stringsAsFactors=FALSE)
if (raw$sim_type == "MH") {
for (i in 1:raw$nClusters) {
if (theta_mon == 1) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (j in 1:raw$nOutcome[i, b]) {
for (c in 1:nchains) {
for (t in 1:(raw$nTreatments - 1)) {
rate <- raw$theta_acc[c, t, i, b, j]/raw$iter
row <- data.frame(chain = c, Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b,j], rate = rate, stringsAsFactors=FALSE)
theta_acc = rbind(theta_acc, row)
}
}
}
}
}
if (gamma_mon == 1) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (j in 1:raw$nOutcome[i, b]) {
for (c in 1:nchains) {
rate <- raw$gamma_acc[c, i, b, j]/raw$iter
row <- data.frame(chain = c, Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b,j], rate = rate, stringsAsFactors=FALSE)
gamma_acc = rbind(gamma_acc, row)
}
}
}
}
}
}
rownames(gamma_conv) <- NULL
rownames(theta_conv) <- NULL
rownames(mu.gamma_conv) <- NULL
rownames(mu.theta_conv) <- NULL
rownames(sigma2.gamma_conv) <- NULL
rownames(sigma2.theta_conv) <- NULL
rownames(gamma_acc) <- NULL
rownames(theta_acc) <- NULL
conv.diag = list(sim_type = raw$sim_type, type = type, monitor = monitor,
gamma.conv.diag = gamma_conv,
theta.conv.diag = theta_conv,
mu.gamma.conv.diag = mu.gamma_conv,
mu.theta.conv.diag = mu.theta_conv,
sigma2.gamma.conv.diag = sigma2.gamma_conv,
sigma2.theta.conv.diag = sigma2.theta_conv,
gamma_acc = gamma_acc,
theta_acc = theta_acc)
attr(conv.diag, "model") = attr(raw, "model")
return(conv.diag)
}
bhpm.cluster.1a.hier2.lev0.print.convergence.summary <- function(conv) {
if (is.null(conv)) {
message("NULL conv data")
return(NULL)
}
# Check which variables we are monitoring
monitor = conv$monitor
theta_mon = monitor[monitor$variable == "theta",]$monitor
gamma_mon = monitor[monitor$variable == "gamma",]$monitor
mu.theta_mon = monitor[monitor$variable == "mu.theta",]$monitor
mu.gamma_mon = monitor[monitor$variable == "mu.gamma",]$monitor
sigma2.theta_mon = monitor[monitor$variable == "sigma2.theta",]$monitor
sigma2.gamma_mon = monitor[monitor$variable == "sigma2.gamma",]$monitor
model = attr(conv, "model")
if (is.null(model)) {
message("Convergence model attribute missing")
return(NULL)
}
if (gamma_mon == 1 && !("gamma.conv.diag" %in% names(conv))) {
message("Missing gamma.conv.diag data")
return(NULL)
}
if (theta_mon == 1 && !("theta.conv.diag" %in% names(conv))) {
message("Missing theta.conv.diag data")
return(NULL)
}
if (mu.gamma_mon == 1 && !("mu.gamma.conv.diag" %in% names(conv))) {
message("Missing mu.gamma.conv.diag data")
return(NULL)
}
if (mu.theta_mon == 1 && !("mu.theta.conv.diag" %in% names(conv))) {
message("Missing mu.theta.conv.diag data")
return(NULL)
}
if (sigma2.gamma_mon == 1 && !("sigma2.gamma.conv.diag" %in% names(conv))) {
message("Missing sigma2.gamma.conv.diag data")
return(NULL)
}
if (sigma2.theta_mon == 1 && !("sigma2.theta.conv.diag" %in% names(conv))) {
message("Missing sigma2.theta.conv.diag data")
return(NULL)
}
if (gamma_mon == 1 && !("gamma_acc" %in% names(conv))) {
message("Missing gamma_acc data")
return(NULL)
}
if (theta_mon == 1 && !("theta_acc" %in% names(conv))) {
message("Missing theta_acc data")
return(NULL)
}
cat(sprintf("Summary Convergence Diagnostics:\n"))
cat(sprintf("================================\n"))
if (conv$type == "Gelman-Rubin") {
if (theta_mon == 1) {
cat(sprintf("theta:\n"))
cat(sprintf("------\n"))
max_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == max(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%d %s %s %s): %0.6f\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome, max_t$stat))
min_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == min(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%d %s, %s %s): %0.6f\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome, min_t$stat))
}
if (gamma_mon == 1) {
cat(sprintf("gamma:\n"))
cat(sprintf("------\n"))
max_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == max(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%s %s %s): %0.6f\n", max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome, max_t$stat))
min_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == min(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%s %s %s): %0.6f\n", min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome, min_t$stat))
}
if (mu.gamma_mon == 1) {
cat(sprintf("mu.gamma:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== max(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%s %s): %0.6f\n", max_t$Cluster, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== min(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%s %s): %0.6f\n", min_t$Cluster, min_t$Outcome.Grp, min_t$stat))
}
if (mu.theta_mon == 1) {
cat(sprintf("mu.theta:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== max(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%d %s %s): %0.6f\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== min(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%d %s %s): %0.6f\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$stat))
}
if (sigma2.gamma_mon == 1) {
cat(sprintf("sigma2.gamma:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== max(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%s %s): %0.6f\n", max_t$Cluster, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== min(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%s %s): %0.6f\n", min_t$Cluster, min_t$Outcome.Grp, min_t$stat))
}
if (sigma2.theta_mon == 1) {
cat(sprintf("sigma2.theta:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== max(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%d %s %s): %0.6f\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== min(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%d %s %s): %0.6f\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$stat))
}
}
else {
if (theta_mon == 1) {
cat(sprintf("theta:\n"))
cat(sprintf("------\n"))
max_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == max(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%d %s %s %s): %0.6f (%s)\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome,
max_t$stat, chk_val(max_t$stat)))
min_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == min(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%d %s %s %s): %0.6f (%s)\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome,
min_t$stat, chk_val(min_t$stat)))
}
if (gamma_mon == 1) {
cat(sprintf("gamma:\n"))
cat(sprintf("------\n"))
max_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == max(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%s %s %s): %0.6f (%s)\n", max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome,
max_t$stat, chk_val(max_t$stat)))
min_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == min(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%s %s %s): %0.6f (%s)\n", min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome,
min_t$stat, chk_val(min_t$stat)))
}
if (mu.gamma_mon == 1) {
cat(sprintf("mu.gamma:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== max(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%s %s): %0.6f (%s)\n", max_t$Cluster, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== min(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%s %s): %0.6f (%s)\n", min_t$Cluster, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (mu.theta_mon == 1) {
cat(sprintf("mu.theta:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== max(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%d %s %s): %0.6f (%s)\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== min(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%d %s %s): %0.6f (%s)\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (sigma2.gamma_mon == 1) {
cat(sprintf("sigma2.gamma:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== max(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%s %s): %0.6f (%s)\n", max_t$Cluster, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== min(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%s %s): %0.6f (%s)\n", min_t$Cluster, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (sigma2.theta_mon == 1) {
cat(sprintf("sigma2.theta:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== max(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%d %s %s): %0.6f (%s)\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== min(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%d %s %s): %0.6f (%s)\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
}
if (conv$sim_type == "MH") {
cat("\nSampling Acceptance Rates:\n")
cat("==========================\n")
if (theta_mon == 1) {
cat("theta:\n")
cat("------\n")
print(sprintf("Min: %0.6f, Max: %0.6f", min(conv$theta_acc$rate),
max(conv$theta_acc$rate)))
}
if (gamma_mon == 1) {
cat("gamma:\n")
cat("------\n")
print(sprintf("Min: %0.6f, Max: %0.6f", min(conv$gamma_acc$rate),
max(conv$gamma_acc$rate)))
}
}
}
rcarragh/bhpm documentation built on Nov. 2, 2020, 5:10 p.m.
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Defines functions bhpm.cluster.1a.hier2.lev0.print.convergence.summary bhpm.cluster.1a.hier2.lev0.convergence.diag
# bhpm.cluster.1a.hier2.lev0.convergence.diag
# R. Carragher
# Date: 29/06/2018
#
# If the MCMC simulation has been run for more than one chain report the Gelman-Rubin statistic.
# If the MCMC simulation has been run for only one chain report the Geweke diagnostic (Z-score)
#
Id <- "$Id: bhpm.cluster.1a.hier2.lev0.convergence.R,v 1.9 2020/03/31 12:42:23 clb13102 Exp clb13102 $"
bhpm.cluster.1a.hier2.lev0.convergence.diag <- function(raw, debug_diagnostic = FALSE)
{
if (is.null(raw)) {
message("NULL raw data")
return(NULL)
}
if (M_global$CLUSTER_check_conv_name_1a_2(raw)) {
message("Missing names");
return(NULL)
}
model = attr(raw, "model")
if (is.null(model)) {
message("Simulation model attribute missing")
return(NULL)
}
# Check which variables we are monitoring
monitor = raw$monitor
theta_mon = monitor[monitor$variable == "theta",]$monitor
gamma_mon = monitor[monitor$variable == "gamma",]$monitor
mu.theta_mon = monitor[monitor$variable == "mu.theta",]$monitor
mu.gamma_mon = monitor[monitor$variable == "mu.gamma",]$monitor
sigma2.theta_mon = monitor[monitor$variable == "sigma2.theta",]$monitor
sigma2.gamma_mon = monitor[monitor$variable == "sigma2.gamma",]$monitor
theta.trt.grps <- raw$Trt.Grps[ raw$Trt.Grps$param == "theta", ]$Trt.Grp
nchains = raw$chains
gamma_conv = data.frame(Cluster = character(0), Outcome.Grp = character(0),
Outcome = character(0), stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
theta_conv = data.frame(Trt.Grp = integer(0), Cluster = character(0), Outcome.Grp = character(0), Outcome = character(0),
stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
mu.gamma_conv = data.frame(Cluster = character(0), Outcome.Grp = character(0),
stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
mu.theta_conv = data.frame(Trt.Grp = integer(0), Cluster = character(0), Outcome.Grp = character(0),
stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
sigma2.gamma_conv = data.frame(Cluster = character(0), Outcome.Grp = character(0),
stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
sigma2.theta_conv = data.frame(Trt.Grp = integer(0), Cluster = character(0), Outcome.Grp = character(0),
stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
type <- NA
if (nchains > 1) {
# Gelman-Rubin Statistics
type = "Gelman-Rubin"
for (i in 1:raw$nClusters) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (j in 1:raw$nOutcome[i, b]) {
# theta
for (t in 1:(raw$nTreatments - 1)) {
if (theta_mon == 1) {
g = M_global$GelmanRubin(raw$theta[, t, i, b, j, ], nchains)
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b,j], stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
theta_conv = rbind(theta_conv, row)
}
}
# gamma
if (gamma_mon == 1) {
g = M_global$GelmanRubin(raw$gamma[, i, b, j, ], nchains)
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b,j], stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
gamma_conv = rbind(gamma_conv, row)
}
}
# mu.gamma
if (mu.gamma_mon == 1) {
g = M_global$GelmanRubin(raw$mu.gamma[, i, b, ], nchains)
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
mu.gamma_conv = rbind(mu.gamma_conv, row)
}
# mu.theta
if (mu.theta_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$GelmanRubin(raw$mu.theta[, t, i, b, ], nchains)
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
mu.theta_conv = rbind(mu.theta_conv, row)
}
}
# sigma2.theta
if (sigma2.theta_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$GelmanRubin(raw$sigma2.theta[, t, i, b, ], nchains)
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
sigma2.theta_conv = rbind(sigma2.theta_conv, row)
}
}
# sigma2.gamma
if (sigma2.gamma_mon == 1) {
g = M_global$GelmanRubin(raw$sigma2.gamma[, i, b, ], nchains)
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
sigma2.gamma_conv = rbind(sigma2.gamma_conv, row)
}
}
}
}
else {
# Geweke Diagnostic
type = "Geweke"
for (i in 1:raw$nClusters) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (j in 1:raw$nOutcome[i, b]) {
# theta
if (theta_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$Geweke(raw$theta[1, t, i, b, j, ])
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b, j], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
theta_conv = rbind(theta_conv, row)
}
}
# gamma
if (gamma_mon == 1) {
g = M_global$Geweke(raw$gamma[1, i, b, j, ])
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b, j], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
gamma_conv = rbind(gamma_conv, row)
}
}
# mu.gamma
if (mu.gamma_mon == 1) {
g = M_global$Geweke(raw$mu.gamma[1, i, b, ])
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
mu.gamma_conv = rbind(mu.gamma_conv, row)
}
# mu.theta
if (mu.theta_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$Geweke(raw$mu.theta[1, t, i, b, ])
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
mu.theta_conv = rbind(mu.theta_conv, row)
}
}
# sigma2.theta
if (sigma2.theta_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$Geweke(raw$sigma2.theta[1, t, i, b, ])
row <- data.frame(Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
sigma2.theta_conv = rbind(sigma2.theta_conv, row)
}
}
# sigma2.gamma
if (sigma2.gamma_mon == 1) {
g = M_global$Geweke(raw$sigma2.gamma[1, i, b, ])
row <- data.frame(Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
sigma2.gamma_conv = rbind(sigma2.gamma_conv, row)
}
}
}
}
theta_acc = data.frame(chain = numeric(0), Trt.Grp = integer(0), Cluster = character(0), Outcome.Grp = character(0),
Outcome = character(0), rate = numeric(0), stringsAsFactors=FALSE)
gamma_acc = data.frame(chain = numeric(0), Cluster = character(0), Outcome.Grp = character(0),
Outcome = character(0), rate = numeric(0), stringsAsFactors=FALSE)
if (raw$sim_type == "MH") {
for (i in 1:raw$nClusters) {
if (theta_mon == 1) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (j in 1:raw$nOutcome[i, b]) {
for (c in 1:nchains) {
for (t in 1:(raw$nTreatments - 1)) {
rate <- raw$theta_acc[c, t, i, b, j]/raw$iter
row <- data.frame(chain = c, Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b,j], rate = rate, stringsAsFactors=FALSE)
theta_acc = rbind(theta_acc, row)
}
}
}
}
}
if (gamma_mon == 1) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (j in 1:raw$nOutcome[i, b]) {
for (c in 1:nchains) {
rate <- raw$gamma_acc[c, i, b, j]/raw$iter
row <- data.frame(chain = c, Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b,j], rate = rate, stringsAsFactors=FALSE)
gamma_acc = rbind(gamma_acc, row)
}
}
}
}
}
}
rownames(gamma_conv) <- NULL
rownames(theta_conv) <- NULL
rownames(mu.gamma_conv) <- NULL
rownames(mu.theta_conv) <- NULL
rownames(sigma2.gamma_conv) <- NULL
rownames(sigma2.theta_conv) <- NULL
rownames(gamma_acc) <- NULL
rownames(theta_acc) <- NULL
conv.diag = list(sim_type = raw$sim_type, type = type, monitor = monitor,
gamma.conv.diag = gamma_conv,
theta.conv.diag = theta_conv,
mu.gamma.conv.diag = mu.gamma_conv,
mu.theta.conv.diag = mu.theta_conv,
sigma2.gamma.conv.diag = sigma2.gamma_conv,
sigma2.theta.conv.diag = sigma2.theta_conv,
gamma_acc = gamma_acc,
theta_acc = theta_acc)
attr(conv.diag, "model") = attr(raw, "model")
return(conv.diag)
}
bhpm.cluster.1a.hier2.lev0.print.convergence.summary <- function(conv) {
if (is.null(conv)) {
message("NULL conv data")
return(NULL)
}
# Check which variables we are monitoring
monitor = conv$monitor
theta_mon = monitor[monitor$variable == "theta",]$monitor
gamma_mon = monitor[monitor$variable == "gamma",]$monitor
mu.theta_mon = monitor[monitor$variable == "mu.theta",]$monitor
mu.gamma_mon = monitor[monitor$variable == "mu.gamma",]$monitor
sigma2.theta_mon = monitor[monitor$variable == "sigma2.theta",]$monitor
sigma2.gamma_mon = monitor[monitor$variable == "sigma2.gamma",]$monitor
model = attr(conv, "model")
if (is.null(model)) {
message("Convergence model attribute missing")
return(NULL)
}
if (gamma_mon == 1 && !("gamma.conv.diag" %in% names(conv))) {
message("Missing gamma.conv.diag data")
return(NULL)
}
if (theta_mon == 1 && !("theta.conv.diag" %in% names(conv))) {
message("Missing theta.conv.diag data")
return(NULL)
}
if (mu.gamma_mon == 1 && !("mu.gamma.conv.diag" %in% names(conv))) {
message("Missing mu.gamma.conv.diag data")
return(NULL)
}
if (mu.theta_mon == 1 && !("mu.theta.conv.diag" %in% names(conv))) {
message("Missing mu.theta.conv.diag data")
return(NULL)
}
if (sigma2.gamma_mon == 1 && !("sigma2.gamma.conv.diag" %in% names(conv))) {
message("Missing sigma2.gamma.conv.diag data")
return(NULL)
}
if (sigma2.theta_mon == 1 && !("sigma2.theta.conv.diag" %in% names(conv))) {
message("Missing sigma2.theta.conv.diag data")
return(NULL)
}
if (gamma_mon == 1 && !("gamma_acc" %in% names(conv))) {
message("Missing gamma_acc data")
return(NULL)
}
if (theta_mon == 1 && !("theta_acc" %in% names(conv))) {
message("Missing theta_acc data")
return(NULL)
}
cat(sprintf("Summary Convergence Diagnostics:\n"))
cat(sprintf("================================\n"))
if (conv$type == "Gelman-Rubin") {
if (theta_mon == 1) {
cat(sprintf("theta:\n"))
cat(sprintf("------\n"))
max_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == max(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%d %s %s %s): %0.6f\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome, max_t$stat))
min_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == min(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%d %s, %s %s): %0.6f\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome, min_t$stat))
}
if (gamma_mon == 1) {
cat(sprintf("gamma:\n"))
cat(sprintf("------\n"))
max_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == max(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%s %s %s): %0.6f\n", max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome, max_t$stat))
min_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == min(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%s %s %s): %0.6f\n", min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome, min_t$stat))
}
if (mu.gamma_mon == 1) {
cat(sprintf("mu.gamma:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== max(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%s %s): %0.6f\n", max_t$Cluster, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== min(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%s %s): %0.6f\n", min_t$Cluster, min_t$Outcome.Grp, min_t$stat))
}
if (mu.theta_mon == 1) {
cat(sprintf("mu.theta:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== max(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%d %s %s): %0.6f\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== min(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%d %s %s): %0.6f\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$stat))
}
if (sigma2.gamma_mon == 1) {
cat(sprintf("sigma2.gamma:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== max(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%s %s): %0.6f\n", max_t$Cluster, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== min(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%s %s): %0.6f\n", min_t$Cluster, min_t$Outcome.Grp, min_t$stat))
}
if (sigma2.theta_mon == 1) {
cat(sprintf("sigma2.theta:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== max(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%d %s %s): %0.6f\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== min(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%d %s %s): %0.6f\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$stat))
}
}
else {
if (theta_mon == 1) {
cat(sprintf("theta:\n"))
cat(sprintf("------\n"))
max_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == max(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%d %s %s %s): %0.6f (%s)\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome,
max_t$stat, chk_val(max_t$stat)))
min_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == min(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%d %s %s %s): %0.6f (%s)\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome,
min_t$stat, chk_val(min_t$stat)))
}
if (gamma_mon == 1) {
cat(sprintf("gamma:\n"))
cat(sprintf("------\n"))
max_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == max(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%s %s %s): %0.6f (%s)\n", max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome,
max_t$stat, chk_val(max_t$stat)))
min_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == min(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%s %s %s): %0.6f (%s)\n", min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome,
min_t$stat, chk_val(min_t$stat)))
}
if (mu.gamma_mon == 1) {
cat(sprintf("mu.gamma:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== max(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%s %s): %0.6f (%s)\n", max_t$Cluster, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== min(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%s %s): %0.6f (%s)\n", min_t$Cluster, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (mu.theta_mon == 1) {
cat(sprintf("mu.theta:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== max(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%d %s %s): %0.6f (%s)\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== min(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%d %s %s): %0.6f (%s)\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (sigma2.gamma_mon == 1) {
cat(sprintf("sigma2.gamma:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== max(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%s %s): %0.6f (%s)\n", max_t$Cluster, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== min(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%s %s): %0.6f (%s)\n", min_t$Cluster, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (sigma2.theta_mon == 1) {
cat(sprintf("sigma2.theta:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== max(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%d %s %s): %0.6f (%s)\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== min(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%d %s %s): %0.6f (%s)\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
}
if (conv$sim_type == "MH") {
cat("\nSampling Acceptance Rates:\n")
cat("==========================\n")
if (theta_mon == 1) {
cat("theta:\n")
cat("------\n")
print(sprintf("Min: %0.6f, Max: %0.6f", min(conv$theta_acc$rate),
max(conv$theta_acc$rate)))
}
if (gamma_mon == 1) {
cat("gamma:\n")
cat("------\n")
print(sprintf("Min: %0.6f, Max: %0.6f", min(conv$gamma_acc$rate),
max(conv$gamma_acc$rate)))
}
}
}
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