R/bonfInfinite.R

In dsrobertson/onlineFDR: Online error rate control

#' Online FDR control based on a Bonferroni-like test
#'
#' This funcion is deprecated, please use \code{\link{Alpha_spending}} instead.
#'
#' Implements online FDR control using a Bonferroni-like test.
#'
#' The function takes as its input either a vector of p-values, or a dataframe
#' with three columns: an identifier (`id'), date (`date') and p-value (`pval').
#' The case where p-values arrive in batches corresponds to multiple instances of
#' the same date. If no column of dates is provided, then the p-values are
#' treated as being ordered in sequence, arriving one at a time.
#'
#' The procedure controls FDR for a potentially infinite stream of p-values by
#' using a Bonferroni-like test. Given an overall significance level
#' \eqn{\alpha}, we choose a (potentially infinite) sequence of non-negative
#' numbers \eqn{\alpha_i} such that they sum to \eqn{\alpha}. Hypothesis \eqn{i}
#' is rejected if the \eqn{i}-th p-value is less than or equal to \eqn{\alpha_i}.
#'
#'
#'@param d Either a vector of p-values, or a dataframe with three columns: an
#'  identifier (`id'), date (`date') and p-value (`pval'). If no column of dates
#'  is provided, then the p-values are treated as being ordered in sequence,
#'  arriving one at a time.
#'
#'@param alpha Overall significance level of the FDR procedure, the default is
#'  0.05.
#'
#'@param alphai Optional vector of \eqn{\alpha_i}, where hypothesis \eqn{i} is
#'  rejected if the \eqn{i}-th p-value is less than or equal to \eqn{\alpha_i}.
#'  A default is provided as proposed by Javanmard and Montanari (2018),
#'  equation 31.
#'
#'@param random Logical. If \code{TRUE} (the default), then the order of the
#'  p-values in each batch (i.e. those that have exactly the same date) is
#'  randomised.
#'
#'@param date.format Optional string giving the format that is used for dates.
#'
#'
#'@return \item{d.out}{ A dataframe with the original data \code{d} (which
#'  will be reordered if there are batches and \code{random = TRUE}), the
#'  adjusted signifcance thresholds \code{alphai} and the indicator function of
#'  discoveries \code{R}, where \code{R[i] = 1} corresponds to hypothesis
#'  \eqn{i} being rejected (otherwise \code{R[i] = 0}).}
#'
#'
#'@references Javanmard, A. and Montanari, A. (2018) Online Rules for Control of
#'  False Discovery Rate and False Discovery Exceedance. \emph{Annals of
#'  Statistics}, 46(2):526-554.
#'
#'@export

bonfInfinite <- function(d, alpha = 0.05, alphai, random = TRUE, date.format = "%Y-%m-%d") {
    # nocov start
    
    .Deprecated("bonfInfinite", package = "onlineFDR", msg = "bonfInfinite is deprecated, please use Alpha_spending instead.")
    
    if (is.data.frame(d)) {
        checkdf(d, random, date.format)
        pval <- d$pval
    } else if (is.vector(d)) {
        pval <- d
    } else {
        stop("d must either be a dataframe or a vector of p-values.")
    }
    
    checkPval(pval)
    N <- length(pval)
    
    if (alpha < 0 || alpha > 1) {
        stop("alpha must be between 0 and 1.")
    }
    
    if (missing(alphai)) {
        alphai <- 0.07720838 * alpha * log(pmax(seq_len(N), 2))/((seq_len(N)) * exp(sqrt(log(seq_len(N)))))
    } else if (any(alphai < 0)) {
        stop("All elements of alphai must be non-negative.")
    } else if (sum(alphai) > alpha) {
        stop("The sum of the elements of alphai must not be greater than alpha.")
    }
    
    R <- as.numeric(pval <= alphai)
    d.out <- data.frame(d, alphai, R)
    
    return(d.out)
}  # nocov end
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