R/plotdata.R

In bamdit: Bayesian Meta-Analysis of Diagnostic Test Data

#' Basic function to plot the data of meta-analysis of diagnostic test
#'
#' This function plots the true positive rates vs the false positive rates of each study included
#' in the meta-analysis. Study results are displayed by circles, the diameter of each circle is proportional
#' to the sample size of the study (or table). If subgroups are displayed each group is represented by
#' different colours. This function use the package \emph{ggplot2}.
#'
#'
#' @param data Either a data frame with at least 4 columns containing the true positives (tp),
#' number of patients with disease (n1), false positives (fp), number of patients without
#' disease (n2), or for two.by.two = TRUE a data frame where each line contains the
#' diagnostic results as a two by two table, where the column names are:
#' TP, FP, TN, FN.
#' @param two.by.two If TRUE indicates that the diagnostic results are given as: TP, FP, TN, FN.
#' @param group a variable name indicating a group factor
#' @param x.lo lower limit of the x-axis
#' @param x.up upper limit of the x-axis
#' @param y.lo lower limit of the y-axis
#' @param y.up upper limit of the y-axis
#' @param alpha.p transparency of the points
#' @param max.size scale parameter of the maximum size
#'
#' @examples
#'
#' ## execute analysis
#' \dontrun{
#'
#' data(ct)
#' ct$design <- with(ct, factor(design,
#'              labels = c("Prospective", "Retrospective")))
#'
#' plotdata(ct,              # Data frame
#'         group = "design", # Groupping variable
#'         y.lo = 0.75,      # Lower limit of y-axis
#'         x.up = 0.75,      # Upper limit of x-axis
#'         alpha.p = 0.5,    # Transparency of the balls
#'         max.size = 5)     # Scale the circles
#'}
#'
#'
#' @import ggplot2


#'@export
plotdata <- function(data,
                     two.by.two = FALSE,
                           group = NULL,
                     x.lo = 0, x.up = 1,
                     y.lo = 0, y.up = 1,
                     alpha.p = 0.7,
                     max.size = 15)
{
  if(two.by.two == FALSE)
    {
  tp <- data[,1]
  n1 <- data[,2]
  fp <- data[,3]
  n2 <- data[,4]
  } else
    {
      tp <- data$TP
      fp <- data$FP
      fn <- data$FN
      tn <- data$TN
      n1 <- tp + fn
      n2 <- fp + tn
    }

  if(tp>n1 || fp>n2)stop("the data is inconsistent")

  if(!missing(data)){
    tpr <-  tp / n1
    fpr <-  fp / n2
    n <- n1 + n2
  }else
    stop("NAs are not alow in this plot function")

  if(is.null(group)){

    dat.plot = data.frame(tpr, fpr, n)
    }
  else{
    dat.plot = data.frame(tpr, fpr, n, gr=data[, group])
    }


  if(!is.null(group)){
    ggplot(dat.plot, aes_string(x = "fpr", y = "tpr", size = "n", group = "gr"))+
      scale_x_continuous(name = "FPR (1 - Specificity)", limits=c(x.lo, x.up)) +
      scale_y_continuous(name = "TPR (Sensitivity)", limits=c(y.lo, y.up)) +
      geom_point(shape = 21, alpha = alpha.p, aes_string(fill = "gr", size = "n")) +
      scale_size_area(max_size = max.size)
  }else{
    ggplot(dat.plot, aes_string(x = "fpr", y = "tpr", size = "n"))+
      scale_x_continuous(name = "FPR (1 - Specificity)", limits=c(x.lo, x.up)) +
      scale_y_continuous(name = "TPR (Sensitivity)", limits=c(y.lo, y.up)) +
      geom_point(shape = 21, fill ="royalblue", alpha = alpha.p) +
      scale_size_area(max_size = max.size)
  }
}