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#' Diagnostics plots for Lambda, Theta, Delta,
#' U, C, Pi, Z and Epsilon. Hazard function, cure proportion and cure time for the median observation.
#'
#' Diagnostic plots for hazard rate (Lambda), regression parameters for the
#' hazard (Theta), regression parameters for the cure rate (Delta), latent
#' variable (U), dependence parameter (C), mean of cure threshold (Mu),
#' cure proportion (Pi), cure threshold (Z) and the
#' parameter of the hierarchical prior (Epsilon).
#'
#' This function returns a diagnosyics plot for which the chain for the selected
#' variable can be monitored. Diagnostics includes trace, ergodic mean,
#' autocorrelation function and histogram.
#'
#' @param M tibble. Contains the output by
#' \code{CCuMRes}.
#' @param variable Either "Lambda", "U", "C", "Mu", "Pi", "Z" or "Epsilon".
#' Variable for which diagnostic plot will be shown.
#' @param pos Positive integer. Position of the selected \code{variable} to be
#' plotted.
#' @seealso \link{CCuMRes}
#' @references Nieto-Barajas, L. E., & Yin, G. (2008). Bayesian semiparametric
#' cure rate model with an unknown threshold. \emph{Scandinavian Journal of
#' Statistics}, \strong{35(3)}, 540-556.
#' https://doi.org/10.1111/j.1467-9469.2007.00589.x
#' @examples
#'
#'
#'
#' ## Simulations may be time intensive. Be patient.
#'
#' ## Example 1
#' # data(BMTKleinbook)
#' # res <- CCuMRes(BMTKleinbook, covs.x = c("tTransplant","hodgkin","karnofsky","waiting"),
#' # covs.y = c("tTransplant","hodgkin","karnofsky","waiting"),
#' # type.t = 2, K = 72, length = 30,
#' # alpha = rep(2,72), beta = rep(2,72), c.r = rep(50, 71), type.c = 2,
#' # var.delta.str = .1, var.theta.str = 1,
#' # var.delta.ini = 100, var.theta.ini = 100,
#' # iterations = 100, burn.in = 10, thinning = 1)
#' # CCuPlotDiag(M = res, variable = "Z")
#' # CCuPlotDiag(M = res, variable = "Pi.m")
#' # CCuPlotDiag(M = res, variable = "Lambda", pos = 2)
#' # CCuPlotDiag(M = res, variable = "U", pos = 4)
#'
#'
#'
#'
#' @export CCuPlotDiag
CCuPlotDiag <-
function(M, variable = "Lambda", pos = 1) {
variable <- match.arg(variable,c("Lambda","Lambda.m","U","C","Theta","Delta","Pi.m","Pi","Z","Z.m","Epsilon"))
K <- extract(M, "K")
if (pos < 0 || pos > K ) {
stop ("Invalid position.")
}
if (pos > (K - 1) && (variable == "U" || variable == "C")) {
stop ("Invalid position.")
}
if (pos > (K) && (variable == "Z" || variable == "Pi")) {
stop ("Invalid observation")
}
if (!("Epsilon" %in% (dplyr::pull(extract(M, c("simulations")), name))) && variable == "Epsilon"){
stop("Plots for 'epsilon' are not available.")
}
if (variable == "Epsilon" && pos != 1) {
warning("'epsilon' has only one entry (1). Graphics shown for epsilon_1.")
pos <- 1
}
if (variable == "Z.m" && pos != 1) {
warning("'Z.m' has only one entry (1). Graphics shown for Z.m_1.")
pos <- 1
}
if (variable == "Pi.m" && pos != 1) {
warning("'Pi.m' has only one entry (1). Graphics shown for Pi.m_1.")
pos <- 1
}
MAT <- extract(M,c("simulations",variable))
if(variable %in% c("Lambda.m")){
MAT <- rlang::set_names(dplyr::select(MAT[[1]],pos), "V1")
} else{
MAT <- rlang::set_names(dplyr::select(tibble::as_tibble(MAT),pos), "V1")
}
var <- switch(variable, Lambda = expression(lambda),
Lambda.m = expression(lambda[median]),
Pi.m = expression(pi),
Epsilon = expression(epsilon),
Theta = expression(theta),
Delta = expression(delta),
Z.m = expression(Z[median]),
Pi = "Pi",
Z = "Z",
U = "U",
C = "C")
title <- paste0("Position: ", pos)
a <- ggplot2::ggplot(MAT) + ggplot2::geom_line(ggplot2::aes(x=seq_len(nrow(MAT)), y = V1), color = "slateblue4") +
ggplot2::labs(x = "Iteration", y = variable) + ggplot2::ylab(var) + ggplot2::ggtitle("Trace")+
ggthemes::theme_tufte() +
ggplot2::theme(axis.line = ggplot2::element_line(colour = "black"))
b <- ggplot2::ggplot(MAT) + ggplot2::geom_line(ggplot2::aes(x=seq_len(nrow(MAT)), y = cumsum(V1)/seq_len(nrow(MAT))), color = "slateblue4") +
ggplot2::labs(x = "Iteration", y = variable) + ggplot2::ggtitle("Ergodic mean") +
ggplot2::ylab(var) +
ggthemes::theme_tufte() +
ggplot2::theme(axis.line = ggplot2::element_line(colour = "black"))
acf.aux <- acf(MAT, plot = F)
c <- ggplot2::ggplot(tibble::as_tibble(cbind(acf.aux$lag, acf.aux$acf))) +
ggplot2::geom_segment(ggplot2::aes(x = V1, xend = V1, y = V2, yend = 0)) +
ggplot2::labs(x = "Lag", y ="ACF")+
ggplot2::ggtitle("Autocorrelation function") +
ggthemes::theme_tufte() +
ggplot2::theme(axis.line = ggplot2::element_line(colour = "black"))
d <- ggplot2::ggplot(MAT) + ggplot2::geom_histogram(ggplot2::aes(x = V1), fill = "lightblue", color = "black", bins = 30) +
ggplot2::ggtitle("Histogram") + ggplot2::xlab(var) + ggplot2::ylab("") +
ggthemes::theme_tufte() +
ggplot2::theme(axis.line = ggplot2::element_line(colour = "black"))
gridExtra::grid.arrange(a,b,c,d, top = title)
}
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