Applied Statistics and Data Mining

文書の過去の版を表示しています。

draw.reliability.data.diagram <- function(x,sort=TRUE,...) {
  if(is.numeric(x)==TRUE) {
    n <- length(x)
    if(sort==TRUE) {
      x.time <- x[sort.list(x,decreasing=TRUE)]
      x.status <- rep(1,n)
      id <- c(1:n)
    } else {
      x.time <- x
      x.status <- rep(1,n)
      id <- c(1:n)
    }
  } else if (is.data.frame(x)==TRUE) {
    n <- dim(x)[1]
    if(sort==TRUE) {
      x.time <- x[sort.list(x[,1],decreasing=TRUE),1]
      x.status <- x[sort.list(x[,1],decreasing=TRUE),2]
      id <- row.names(x)[sort.list(x[,1],decreasing=TRUE)]
    } else {
      x.time <- x[,1]
      x.status <- x[,2]
      id <- row.names(x)
    }
  } else if (is.matrix(x)==TRUE) {
    n <- dim(x)[1]
    if(sort==TRUE) {
      x.time <- x[sort.list(x[,1],decreasing=TRUE),1]
      x.status <- x[sort.list(x[,1],decreasing=TRUE),2]
      id <- rawnames(x)[sort.list(x[,1],decreasing=TRUE)]
    } else {
      x.time <- x[,1]
      x.status <- x[,2]
      id <- rawnames(x)
    }
  }
  plot(x.time,c(1:n),
       xlim=c(0,max(x.time)),
       type="n",
       ylab="#",
       xlab="Lifetime data",yaxt="n")
  points(x.time,c(1:n),pch=c(1,4)[x.status+1],...)
  for( i in c(1:n) ) {
    lines(c(0,x.time[i]),c(i,i),...)
  }
}