#' Plot the confidence intervals, and the confidence interval determination procedures
#' performance, in a group sequential single-arm trial design for a single binary endpoint
#'
#' Plots the confidence intervals, and the performance of the confidence interval determination procedures,
#' in a group sequential single-arm trial design for a single binary endpoint
#' determined using \code{ci_gs()}. A range of plots are available, of which
#' the confidence intervals and the coverage probability curve will be printed by default.
#'
#' @param x An object of class \code{"sa_ci_gs"}, as returned by \code{ci_gs()}.
#' @param ... Included for compatibility with the generic. Not currently used.
#' @param output A logical variable indicating whether the outputs described below
#' should be returned.
#' @return If \code{output = TRUE}, a list containing the following elements is returned
#' \itemize{
#' \item A list in the slot \code{$plot_des} containing the available plots.
#' \item Each of the input variables as specified, subject to internal modification.
#' }
#' @examples
#' # Find the optimal group sequential design for the default parameters
#' des <- des_gs()
#' # Determine the performance of the confidence interval determination procedures for a range of
#' # possible response probabilities
#' ci <- ci_gs(des, pi = seq(0, 1, 0.01))
#' # Plot the confidence intervals and the determination procedures performance
#' plot(ci)
#' @seealso \code{\link{des_gs}}, \code{\link{opchar_gs}}, \code{\link{est_gs}},
#' \code{\link{pval_gs}}, \code{\link{ci_gs}}, and their associated \code{plot}
#' family of functions.
#' @export
plot.sa_ci_gs <- function(x, ..., output = F) {
ci <- x
##### Input Checking #########################################################
check_sa_ci_gs(ci)
check_logical(output, "output")
##### Main Computations ######################################################
plot_ci <- list()
new_levels <- levels(ci$ci$method)
for (i in 1:length(new_levels)) {
if (new_levels[i] == "exact") {
new_levels[i] <- "Exact"
} else if (new_levels[i] == "mid_p") {
new_levels[i] <- "Mid-p"
} else {
new_levels[i] <- "Naive"
}
}
ci$ci$method <- plyr::mapvalues(ci$ci$method,
from = levels(ci$ci$method),
to = new_levels)
local_ci <- tidyr::gather(ci$ci, key = "limit", value = "pi",
`clow(s,m)`:`cupp(s,m)`)
local_ci$k <- plyr::mapvalues(ci$ci$k, from = levels(ci$ci$k),
to = paste("k =", levels(ci$ci$k)))
plot_ci$ci <- ggplot2::ggplot() +
ggplot2::xlab(expression(italic(s))) +
ggplot2::ylab(expression(paste(italic(c)[low], "(", italic(s),
",", italic(n), "), ",
italic(c)[upp], "(", italic(s),
",", italic(n), ")",
sep = ""))) +
ggplot2::geom_hline(yintercept = c(ci$des$pi0, ci$des$pi1),
linetype = 2) +
ggplot2::geom_point(data = dplyr::filter(local_ci,
limit == "clow(s,m)"),
ggplot2::aes(x = s, y = pi,
color = method)) +
ggplot2::geom_point(data = dplyr::filter(local_ci,
limit == "cupp(s,m)"),
ggplot2::aes(x = s, y = pi,
color = method)) +
ggplot2::geom_line(data = dplyr::filter(local_ci,
limit == "clow(s,m)"),
ggplot2::aes(x = s, y = pi,
color = method)) +
ggplot2::geom_line(data = dplyr::filter(local_ci,
limit == "cupp(s,m)"),
ggplot2::aes(x = s, y = pi,
color = method)) +
ggthemes::scale_color_ptol("method") +
theme_singlearm() + ggplot2::facet_grid(.~k)
print(plot_ci$ci)
plot_ci$l <- ggplot2::ggplot() +
ggplot2::geom_point(data = ci$ci,
ggplot2::aes(x = s, y = `l(s,m)`,
color = method)) +
ggplot2::geom_line(data = ci$ci,
ggplot2::aes(x = s, y = `l(s,m)`,
color = method)) +
ggplot2::xlab(expression(italic(s))) +
ggplot2::ylab(expression(italic(l), "(", italic(s), ",", italic(m), ")")) +
ggthemes::scale_color_ptol("method") +
theme_singlearm() + ggplot2::facet_grid(.~k)
if (is.null(ci$perf)) {
perf <- tibble::tibble(method = factor(unique(ci$ci$method),
levels = unique(ci$ci$method)),
`bar(L)` = NA, `max(L)` = NA)
for (i in 1:nrow(perf)) {
ci_i <- dplyr::filter(ci$ci, method == ci$ci$method[i])
perf$`bar(L)`[i] <- mean(ci_i$`l(s,m)`)
perf$`max(L)`[i] <- max(ci_i$`l(s,m)`)
}
plot_ci$`bar(L)` <- ggplot2::ggplot(data = perf,
ggplot2::aes(x = method, y = `bar(L)`,
fill = method)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::xlab("Method") +
ggplot2::ylab(expression(bar(italic(L)))) +
ggthemes::scale_fill_ptol("method") +
theme_singlearm() +
ggplot2::theme(axis.text.x =
ggplot2::element_text(angle = 45,
hjust = 1),
legend.position = "none")
plot_ci$`max(L)` <- ggplot2::ggplot(data = perf,
ggplot2::aes(x = method, y = `max(L)`,
fill = method)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::xlab("Method") +
ggplot2::ylab(expression(paste(italic(max), "(",
italic(L), ")",
sep = ""))) +
ggthemes::scale_fill_ptol("method") +
theme_singlearm() +
ggplot2::theme(axis.text.x =
ggplot2::element_text(angle = 45,
hjust = 1),
legend.position = "none")
} else {
new_levels <- levels(ci$perf$method)
for (i in 1:length(new_levels)) {
if (new_levels[i] == "exact") {
new_levels[i] <- "Exact"
} else if (new_levels[i] == "mid_p") {
new_levels[i] <- "Mid-p"
} else {
new_levels[i] <- "Naive"
}
}
ci$perf$method <- plyr::mapvalues(ci$perf$method,
from = levels(ci$perf$method),
to = new_levels)
plot_ci$`bar(L)` <- ggplot2::ggplot(data = dplyr::filter(ci$perf,
pi ==
ci$perf$pi[1]),
ggplot2::aes(x = method,
y = `bar(L)`,
fill = method)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::xlab("Method") +
ggplot2::ylab(expression(bar(italic(L)))) +
ggthemes::scale_fill_ptol("method") +
theme_singlearm() +
ggplot2::theme(axis.text.x =
ggplot2::element_text(angle = 45,
hjust = 1),
legend.position = "none")
plot_ci$`max(L)` <- ggplot2::ggplot(data = dplyr::filter(ci$perf,
pi ==
ci$perf$pi[1]),
ggplot2::aes(x = method,
y = `max(L)`,
fill = method)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::xlab("Method") +
ggplot2::ylab(expression(paste(italic(max), "(",
italic(L), ")",
sep = ""))) +
ggthemes::scale_fill_ptol("method") +
theme_singlearm() +
ggplot2::theme(axis.text.x =
ggplot2::element_text(angle = 45,
hjust = 1),
legend.position = "none")
if (min(ci$pi) < ci$des$des$pi0) {
red <- tibble::tibble(start = min(ci$pi),
end = min(ci$des$des$pi0, max(ci$pi)))
}
if (all(min(ci$pi) <= ci$des$des$pi1,
max(ci$pi) >= ci$des$des$pi0)) {
amber <- tibble::tibble(start = max(ci$des$des$pi0, min(ci$pi)),
end = min(ci$des$des$pi1, max(ci$pi)))
}
if (max(ci$pi) > ci$des$des$pi1) {
green <- tibble::tibble(start = max(ci$des$des$pi1,
min(ci$pi)), end = max(ci$pi))
}
plot_ci$`E(L|pi)` <- ggplot2::ggplot()
if (min(ci$pi) < ci$des$des$pi0) {
plot_ci$`E(L|pi)` <- plot_ci$`E(L|pi)` +
ggplot2::geom_rect(data = red,
ggplot2::aes(xmin = start,
xmax = end,
ymin = -Inf,
ymax = Inf),
alpha = 0.1,
fill = "firebrick2")
}
if (all(min(ci$pi) <= ci$des$des$pi1,
max(ci$pi) >= ci$des$des$pi0)) {
plot_ci$`E(L|pi)` <- plot_ci$`E(L|pi)` +
ggplot2::geom_rect(data = amber,
ggplot2::aes(xmin = start,
xmax = end,
ymin = -Inf,
ymax = Inf),
alpha = 0.1,
fill = "orange")
}
if (max(ci$pi) > ci$des$des$pi1) {
plot_ci$`E(L|pi)` <- plot_ci$`E(L|pi)` +
ggplot2::geom_rect(data = green,
ggplot2::aes(xmin = start,
xmax = end,
ymin = -Inf,
ymax = Inf),
alpha = 0.1,
fill = "green4")
}
plot_ci$`E(L|pi)` <- plot_ci$`E(L|pi)` +
ggplot2::geom_line(data = ci$perf,
ggplot2::aes(x = pi,
y = `E(L|pi)`,
color = method)) +
ggplot2::xlab(expression(pi)) +
ggplot2::ylab(expression(paste("E(", italic(L), "|",
pi, ")", sep = ""))) +
ggthemes::scale_color_ptol("method") +
theme_singlearm() +
ggplot2::scale_x_continuous(expand = c(0, 0),
limits = c(min(ci$perf$pi),
max(ci$perf$pi)))
plot_ci$`Var(L|pi)` <- ggplot2::ggplot()
if (min(ci$pi) < ci$des$des$pi0) {
plot_ci$`Var(L|pi)` <- plot_ci$`Var(L|pi)` +
ggplot2::geom_rect(data = red,
ggplot2::aes(xmin = start,
xmax = end,
ymin = -Inf,
ymax = Inf),
alpha = 0.1,
fill = "firebrick2")
}
if (all(min(ci$pi) <= ci$des$des$pi1,
max(ci$pi) >= ci$des$des$pi0)) {
plot_ci$`Var(L|pi)` <- plot_ci$`Var(L|pi)` +
ggplot2::geom_rect(data = amber,
ggplot2::aes(xmin = start,
xmax = end,
ymin = -Inf,
ymax = Inf),
alpha = 0.1,
fill = "orange")
}
if (max(ci$pi) > ci$des$des$pi1) {
plot_ci$`Var(L|pi)` <- plot_ci$`Var(L|pi)` +
ggplot2::geom_rect(data = green,
ggplot2::aes(xmin = start,
xmax = end,
ymin = -Inf,
ymax = Inf),
alpha = 0.1,
fill = "green4")
}
plot_ci$`Var(L|pi)` <- plot_ci$`Var(L|pi)` +
ggplot2::geom_line(data = ci$perf,
ggplot2::aes(x = pi,
y = `Var(L|pi)`,
color = method)) +
ggplot2::xlab(expression(pi)) +
ggplot2::ylab(expression(paste(italic(Var), "(",
italic(L), "|", pi,
")", sep = ""))) +
ggthemes::scale_color_ptol("method") +
theme_singlearm() +
ggplot2::scale_x_continuous(expand = c(0, 0),
limits = c(min(ci$perf$pi),
max(ci$perf$pi)))
plot_ci$`Cover(C|pi)` <- ggplot2::ggplot()
if (min(ci$pi) < ci$des$des$pi0) {
plot_ci$`Cover(C|pi)` <- plot_ci$`Cover(C|pi)` +
ggplot2::geom_rect(data = red,
ggplot2::aes(xmin = start,
xmax = end,
ymin = -Inf,
ymax = Inf),
alpha = 0.1,
fill = "firebrick2")
}
if (all(min(ci$pi) <= ci$des$des$pi1,
max(ci$pi) >= ci$des$des$pi0)) {
plot_ci$`Cover(C|pi)` <- plot_ci$`Cover(C|pi)` +
ggplot2::geom_rect(data = amber,
ggplot2::aes(xmin = start,
xmax = end,
ymin = -Inf,
ymax = Inf),
alpha = 0.1,
fill = "orange")
}
if (max(ci$pi) > ci$des$des$pi1) {
plot_ci$`Cover(C|pi)` <- plot_ci$`Cover(C|pi)` +
ggplot2::geom_rect(data = green,
ggplot2::aes(xmin = start,
xmax = end,
ymin = -Inf,
ymax = Inf),
alpha = 0.1,
fill = "green4")
}
plot_ci$`Cover(C|pi)` <- plot_ci$`Cover(C|pi)` +
ggplot2::geom_hline(yintercept =
1 - ci$des$alpha,
linetype = 2) +
ggplot2::geom_line(data = ci$perf,
ggplot2::aes(x = pi,
y = `Cover(C|pi)`,
color = method)) +
ggplot2::xlab(expression(pi)) +
ggplot2::ylab(expression(paste(italic(Cover), "(",
italic(C), "|", pi,
")", sep = ""))) +
ggthemes::scale_color_ptol("method") +
theme_singlearm() +
ggplot2::scale_x_continuous(expand = c(0, 0),
limits = c(min(ci$perf$pi),
max(ci$perf$pi)))
print(plot_ci$`Cover(C|pi)`)
}
##### Outputting #############################################################
if (output) {
return(list(plot_ci = plot_ci, ci = ci))
}
}
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