R/utility.R

In luyouepiusf/ClusteringTree4: Supervised clustering with survival outcomes

num_to_str_g<-function(x,digits=4)sprintf(paste0("%.",digits,"g"),x)
num_to_str_f<-function(x,digits=2)sprintf(paste0("%.",digits,"f"),x)
num_to_pvalue<-function(x,digits=3){
  ifelse(x<0.001,"p<0.001",paste0("p=",sprintf(paste0("%.",digits,"f"),x)))
}
create_dictionary<-function(x,na.rm=T){
  x<-sort(na.omit(unique(x)))
  structure(1:length(x),names=x)
}
dostep <- function(x, y) {
  ### create a step function based on x, y coordinates
  ### modified from `survival:print.survfit'
  if (is.na(x[1] + y[1])) {
    x <- x[-1]
    y <- y[-1]
  }
  n <- length(x)
  if (n > 2) {  
    # replace verbose horizonal sequences like
    # (1, .2), (1.4, .2), (1.8, .2), (2.3, .2), (2.9, .2), (3, .1)
    # with (1, .2), (3, .1).  They are slow, and can smear the looks
    # of the line type.
    dupy <- c(TRUE, diff(y[-n]) !=0, TRUE)
    n2 <- sum(dupy)
    #create a step function
    xrep <- rep(x[dupy], c(1, rep(2, n2-1)))
    yrep <- rep(y[dupy], c(rep(2, n2-1), 1))
    RET <- list(x = xrep, y = yrep)
  } else {
    if (n == 1) {
      RET <- list(x = x, y = y)
    } else {
      RET <- list(x = x[c(1,2,2)], y = y[c(1,1,2)])
    }
  }
  return(RET)
}