R/BePloth.R

In BGPhazard: Markov Beta and Gamma Processes for Modeling Hazard Rates

#' Plots for the discrete Hazard and Survival Function Estimates
#' 
#' Plots the resulting hazard function along with the survival function
#' estimates defined by the Markov beta process (Nieto-Barajas and Walker,
#' 2002).
#' 
#' This function returns estimators plots for the hazard rate as computed
#' by \code{\link{BeMRes}} together with the Nelson-Aalen estimate along with their
#' confidence intervals for the data set given. Additionally, it plots the
#' survival function and the Kaplan-Meier estimate with their corresponding
#' credible intervals.
#' 
#' @param M tibble. Contains the output generated by \code{BeMRres}.
#' @param type.h character, "line" = plots the hazard rate of each interval
#' joined by a line, "dot" = plots the hazard rate of each interval with a dot.
#' @param add.survival logical, If \code{TRUE}, plots the Nelson-Alen based
#' estimate in the same graphic of the hazard rate and the Kaplan-Meier
#' estimates of the survival function.
#' @param intervals logical. If TRUE, plots confidence bands for the selected functions including Nelson-Aalen and/or Kaplan-Meier estimate.
#' @param confidence Numeric. Confidence band width.
#' @param summary Logical. If \code{TRUE}, a summary for hazard and survival
#' functions is returned as a tibble.
#' @return \item{SUM.h}{Numeric tibble. Summary for the mean, median, and a
#' \code{confint / 100} confidence interval for each failure time of the hazard
#' function.} \item{SUM.S}{Numeric tibble. Summary for the mean, median, and a
#' \code{confint / 100} confidence interval for each failure time of the survival
#' function.}
#' @seealso \link{BeMRes}, \link{BePlotDiag}
#' @references - Nieto-Barajas, L. E. & Walker, S. G. (2002). Markov beta and
#' gamma processes for modelling hazard rates. \emph{Scandinavian Journal of
#' Statistics} \strong{29}: 413-424.
#' @examples
#' 
#' 
#' 
#' ## Simulations may be time intensive. Be patient.
#' 
#' ## Example 1
#' #  data(psych)
#' #  timesP <- psych$time
#' #  deltaP <- psych$death
#' #  BEX1 <- BeMRes(timesP, deltaP, iterations = 3000, burn.in = 300, thinning = 1)
#' #  BePloth(BEX1)
#' #  sum <- BePloth(BEX1, type.h = "line", summary = T)
#' 
#' ## Example 2
#' #  data(gehan)
#' #  timesG <- gehan$time[gehan$treat == "control"]
#' #  deltaG <- gehan$cens[gehan$treat == "control"]
#' #  BEX2 <- BeMRes(timesG, deltaG, type.c = 2, c.r = rep(50, 22))
#' #  BePloth(BEX2)
#' 
#' 
#' 
#' @export BePloth
BePloth <-
  function(M, type.h = "dot", add.survival = T, intervals = T,
           confidence = 0.95, summary = FALSE) {
    SUM <- PiSumm(M, confidence)
    s <- SUM %>% tibble::deframe()
    v <- list("tao",
              "K",
              "times",
              "delta"
    ) %>% purrr::map(~extract(M,.x)) %>% rlang::set_names(c("tao","K","times","delta"))
    tao <- v$tao
    K <- v$K
    delta <- v$delta
    times <- v$times
    
    if(type.h == "dot") {
      h <- s$SUM.h %>% ggplot2::ggplot() + 
        ggplot2::geom_point(ggplot2::aes(x = tao[-1], 
                                         y = mean, color = "Hazard Function")) + 
        ggplot2::scale_color_manual(values = c("black","#b22222")) +
        ggplot2::guides(color = ggplot2::guide_legend(title = "")) +
        ggplot2::guides(color = ggplot2::guide_legend(title = "")) +
        ggplot2::xlab("Time") + ggplot2::ylab("Hazard rate") + ggplot2::scale_alpha_continuous(guide = F) + 
        ggplot2::ggtitle(paste0("Estimate of hazard rates with intervals at ",confidence * 100,"% of credibility")) +
        ggthemes::theme_tufte() +
        ggplot2::theme(axis.line = ggplot2::element_line(colour = "black"),
                       legend.position="bottom")
      if(intervals){
        h <- h + ggplot2::geom_errorbar(ggplot2::aes(ymin = lower, ymax = upper, x = tao[-1]), 
                                        alpha = 0.5, color = "gray50", width = 1)  
      } 
    }
    if(type.h == "line"){ 
      h <- s$SUM.h %>% ggplot2::ggplot() + 
        ggplot2::geom_line(ggplot2::aes(x = (tao[-(K+1)] + tao[-1])/2, y = mean, color = "Hazard Function")) +
        ggplot2::scale_color_manual(values = c("black","#b22222")) +
        ggplot2::guides(color = ggplot2::guide_legend(title = "")) +
        ggplot2::xlab("Time") + ggplot2::ylab("Hazard rate") + ggplot2::scale_alpha_continuous(guide = F) + 
        ggplot2::ggtitle(paste0("Estimate of hazard rates with intervals at ",confidence * 100,"%  of credibility")) +
        ggthemes::theme_tufte() +
        ggplot2::theme(axis.line = ggplot2::element_line(colour = "black"),
                       legend.position="bottom")
      if(intervals){
        h <- h + ggplot2::geom_ribbon(ggplot2::aes(x = (tao[-(K+1)] + tao[-1])/2, ymin = lower, ymax = upper), alpha = .5, fill = "gray70")
      }
    }
    
    S <- s$SUM.S %>% ggplot2::ggplot() + ggplot2::geom_step(na.rm = T, ggplot2::aes(x = t, y = `S^(t)`,color = "Model estimate")) + 
      ggplot2::scale_color_manual(limits = c("Model estimate","Kaplan-Meier"),values = c("black","#b22222")) +
      ggplot2::guides(color = ggplot2::guide_legend(title = "")) +
      ggplot2::scale_y_continuous(limits = c(0,1)) + 
      ggplot2::ggtitle(paste0("Estimate of Survival Function with intervals at ", confidence * 100,"%  of credibility")) +
      ggplot2::labs(x = "t",
                    y = expression(S^{(t)})) +
      ggthemes::theme_tufte() +
      ggplot2::theme(axis.line = ggplot2::element_line(colour = "black"),
                     legend.position = "bottom")
    if(intervals){
      S <- S + ggplot2::geom_step(na.rm = T, ggplot2::aes(x = t, y = lower), alpha = 0.5, linetype = "dashed") + 
        ggplot2::geom_step(na.rm = T, ggplot2::aes(x = t, y = upper), alpha = 0.5, linetype = "dashed")
    }
    
    if(add.survival){
      fit <- survival::survfit(survival::Surv(time = times, event = delta) ~ 1, 
                               conf.int = confidence)
      km.data <- tibble::tibble(time = fit$time,surv = fit$surv, lower = fit$lower,
                                upper = fit$upper)
      if(km.data$time[1]!= 0){
        km.data <- dplyr::bind_rows(tibble::tibble(time = 0, surv = 1, lower = 1, upper = 1),km.data)
      }
      na.data <- tibble::tibble(time = fit$time, h.est = fit$n.event / fit$n.risk)
      h <- h + ggplot2::geom_point(data = na.data, ggplot2::aes(x = time, y = h.est, color = "Nelson-Aalen based estimate"))
      
      S <- S + ggplot2::geom_step(data = km.data, na.rm = T, ggplot2::aes(x = time,y = surv, color = "Kaplan-Meier")) 
      if(intervals){
        S <- S + ggplot2::geom_step(data = km.data, na.rm = T, ggplot2::aes(x = time, y = lower), alpha = 0.5, color = "#b22222", linetype = "dashed") + 
          ggplot2::geom_step(data = km.data, na.rm = T, ggplot2::aes(x = time, y = upper), alpha = 0.5, color = "#b22222", linetype = "dashed") 
      }
    }
    
    if (summary == TRUE) {
      return(list(h,S,SUM))
    } else{
      return(list(h,S))
    }
  }