R/BatchPRDS.R

In dsrobertson/onlineFDR: Online error rate control

#' BatchPRDS: Online batch FDR control under Positive Dependence
#'
#' Implements the BatchPRDS algorithm for online FDR control, where PRDS stands
#' for positive regression dependency on a subset, as presented by Zrnic et al.
#' (2020).
#'
#' The function takes as its input a dataframe with three columns: identifiers
#' (`id'), batch numbers (`batch') and p-values (`pval').
#'
#' The BatchPRDS algorithm controls the FDR when the p-values in one batch are
#' positively dependent, and independent across batches. Given an overall
#' significance level \eqn{\alpha}, we choose a sequence of non-negative numbers
#' \eqn{\gamma_i} such that they sum to 1. The algorithm runs the
#' Benjamini-Hochberg procedure on each batch, where the values of the adjusted
#' significance thresholds \eqn{\alpha_{t+1}} depend on the number of previous 
#' discoveries.
#'
#' Further details of the BatchPRDS algorithm can be found in Zrnic et al. 
#' (2020).
#'
#' @param d A dataframe with three columns: identifiers (`id'),
#'   batch numbers (`batch') and p-values (`pval').
#'
#' @param alpha Overall significance level of the FDR procedure, the default is
#'   0.05.
#'
#' @param gammai Optional vector of \eqn{\gamma_i}. A default is provided with
#'   \eqn{\gamma_j} proportional to \eqn{1/j^(1.6)}.
#'   
#' @param display_progress Logical. If \code{TRUE} prints out a progress bar for the algorithm runtime. 
#'
#' @return \item{out}{ A dataframe with the original data \code{d} and the
#'   indicator function of discoveries \code{R}. Hypothesis \eqn{i} is rejected
#'   if the \eqn{i}-th p-value within the \eqn{t}-th batch is less than or equal
#'   to \eqn{(r/n)\alpha_t}, where \eqn{r} is the rank of the \eqn{i}-th p-value
#'   within an ordered set and \eqn{n} is the total number of hypotheses within
#'   the \eqn{t}-th batch. If hypothesis \eqn{i} is rejected, \code{R[i] = 1}
#'   (otherwise \code{R[i] = 0}).}
#'
#' @references Zrnic, T., Jiang D., Ramdas A. and Jordan M. (2020). The Power of
#'   Batching in Multiple Hypothesis Testing. \emph{International Conference on
#'   Artificial Intelligence and Statistics}: 3806-3815
#'
#' @examples
#'
#' sample.df <- data.frame(
#' id = c('A15432', 'B90969', 'C18705', 'B49731', 'E99902',
#'     'C38292', 'A30619', 'D46627', 'E29198', 'A41418',
#'     'D51456', 'C88669', 'E03673', 'A63155', 'B66033'),
#' pval = c(2.90e-08, 0.06743, 0.01514, 0.08174, 0.00171,
#'         3.60e-05, 0.79149, 0.27201, 0.28295, 7.59e-08,
#'         0.69274, 0.30443, 0.00136, 0.72342, 0.54757),
#' batch = c(rep(1,5), rep(2,6), rep(3,4)))
#'
#' BatchPRDS(sample.df)
#'
#' @export

BatchPRDS <- function(d, alpha = 0.05, gammai, display_progress = FALSE){
  
  d <- checkPval(d)
  
  if (!is.data.frame(d)) {
    stop("d must be a dataframe")
  } else if (!("batch" %in% colnames(d))) {
    stop("d needs to have a column called batch")
  }
  
  if (alpha < 0 || alpha > 1) {
    stop("alpha must be between 0 and 1.")
  }
  
  if(!is.numeric(d$batch)) {
    stop("Check that your batch labels are numeric values.")
  }
  
  #check that batches were labeled correctly
  
  if(is.unsorted(d$batch)) {
    d <- d[order(d$batch),]
    warning("Batches were re-ordered in increasing numeric value.")
  }
  
  n_batch <- length(unique(d$batch))
  if (missing(gammai)) {
    gammai <- 0.4374901658/(seq_len(n_batch)^(1.6))
  } else if (any(gammai < 0)) {
    stop("All elements of gammai must be non-negative.")
  } else if (sum(gammai) > 1) {
    stop("The sum of the elements of gammai must not be greater than 1.")
  }
  
  ### Start Batch PRDS procedure
  
  R <- NULL
  alphai <- rep(0, n_batch)
  alphai[1] <- gammai[1] * alpha
  
  nt <- as.vector(table(d$batch))
  batch_indices <- c(0, cumsum(nt))
  
  if(display_progress) {
    pb <- progress::progress_bar$new(format = "  Computing [:bar] :percent eta: :eta",
                                     total = n_batch, clear = FALSE, width = 60)
    
    for(i in seq_len(n_batch)){
      pb$tick()
      idx_b <- batch_indices[i]+1
      idx_e <- batch_indices[i+1]
      batch_pval <- .subset2(d, "pval")[idx_b:idx_e]
      
      j <- nt[i]:1L
      
      o <- order(batch_pval, decreasing = TRUE)
      ro <- order(o)
      out_R <- pmin(1, cummin(nt[i]/j * batch_pval[o]))[ro] <= alphai[i]
      
      R <- c(R, out_R)
      
      #update alphai
      if(i < n_batch) {
        alphai[i+1] <- alpha * (gammai[i+1]/nt[i+1]) * (nt[i+1] + sum(R))
      }
      
    }
    out <- d
    out$R <- as.numeric(R)
    out$alphai <- rep(alphai, nt)
    out
  } else {
    for(i in seq_len(n_batch)){
      idx_b <- batch_indices[i]+1
      idx_e <- batch_indices[i+1]
      batch_pval <- .subset2(d, "pval")[idx_b:idx_e]
      
      j <- nt[i]:1L
      
      o <- order(batch_pval, decreasing = TRUE)
      ro <- order(o)
      out_R <- pmin(1, cummin(nt[i]/j * batch_pval[o]))[ro] <= alphai[i]
      
      R <- c(R, out_R)
      
      #update alphai
      if(i < n_batch) {
        alphai[i+1] <- alpha * (gammai[i+1]/nt[i+1]) * (nt[i+1] + sum(R))
      }
      
    }
    out <- d
    out$R <- as.numeric(R)
    out$alphai <- rep(alphai, nt)
    out
  }
}