update to v1.0.0

R/FWERSD.R

@@ -13,13 +13,21 @@
 #' @seealso \code{\link{TH.p.adjust}}, \code{\link[stats]{p.adjust}}.
 #' @author Yalin Zhu
 #' @references
+#' Zhu, Y., & Guo, W. (2017).
+#' Familywise error rate controlling procedures for discrete data
+#' \emph{arXiv preprint} arXiv:1711.08147.
+#'
 #' Holm, S. (1979).
 #' A simple sequentially rejective multiple test procedure.
 #' \emph{Scandinavian Journal of Statistics}, \strong{6}: 65-70.
 #' @examples
 #' p <- c(pbinom(1,8,0.5),pbinom(1,5,0.75),pbinom(1,6,0.6))
 #' p.set <-list(pbinom(0:8,8,0.5),pbinom(0:5,5,0.75),pbinom(0:6,6,0.6))
 #' MHolm.p.adjust(p,p.set)
+#' ## Compare with the traditional Holm adjustment
+#' p.adjust(p,method = "holm")
+#' ## Compare with the Tarone-Holm adjustment
+#' TH.p.adjust(p,p.set)
 #' @export
 
 MHolm.p.adjust <- function(p,p.set, alpha = 0.05, make.decision = FALSE){

R/FWERSS.R

@@ -40,10 +40,18 @@ Sidak.p.adjust <- function(p, alpha = 0.05, make.decision = FALSE){
 #' A numeric vector of the adjusted p-values (of the same length as \code{p}) if  \code{make.decision = FALSE}, or a list including original p-values, adjusted p-values and decision rules if \code{make.decision = TRUE}.
 #' @seealso \code{\link{Tarone.p.adjust}},  \code{\link{MixBonf.p.adjust}},  \code{\link[stats]{p.adjust}}.
 #' @author  Yalin Zhu
+#' @references
+#' Zhu, Y., & Guo, W. (2017).
+#' Familywise error rate controlling procedures for discrete data
+#' \emph{arXiv preprint} arXiv:1711.08147.
 #' @examples
 #' p <- c(pbinom(1,8,0.5),pbinom(1,5,0.75),pbinom(1,6,0.6))
 #' p.set <-list(pbinom(0:8,8,0.5),pbinom(0:5,5,0.75),pbinom(0:6,6,0.6))
 #' MBonf.p.adjust(p,p.set)
+#' ## Compare with the traditional Bonferroni adjustment
+#' p.adjust(p,method = "bonferroni")
+#' ## Compare with the Tarone adjustment
+#' Tarone.p.adjust(p,p.set)
 #' @export
 
 MBonf.p.adjust <- function(p, p.set, alpha = 0.05, make.decision = FALSE){
@@ -78,6 +86,17 @@ MBonf.p.adjust <- function(p, p.set, alpha = 0.05, make.decision = FALSE){
 #' @seealso \code{\link{Tarone.p.adjust}},  \code{\link{MBonf.p.adjust}},  \code{\link[stats]{p.adjust}}.
 #' @note The arguments include three parts, the available p-values need to be reorganized in advance. Gather all available p-values for continuous data as \code{pc}, and all available p-values for discrete data as \code{pd}. The attainable p-value refers to the element of domain set of p-value for the corresponding hypothesis for discrete test statistics, the p-value can only take finite values bewtween 0 and 1, that is, the attainable p-values for discrete case are finite and countable, so we can assign them in a finite list \code{pd.set}. The function returns the  adjusted p-values with the first part for continuous data of the same length as \code{pc}, and second part for discrete data of the same length as \code{pd}
 #' @author  Yalin Zhu
+#' @references
+#' Zhu, Y., & Guo, W. (2017).
+#' Familywise error rate controlling procedures for discrete data
+#' \emph{arXiv preprint} arXiv:1711.08147.
+#'
+#' @examples
+#' pd <- c(pbinom(1,8,0.5),pbinom(1,5,0.75)); pc <- c(0.04, 0.1)
+#' pd.set <-list(pbinom(0:8,8,0.5),pbinom(0:5,5,0.75))
+#' MixBonf.p.adjust(pc,pd,pd.set)
+#' ## Compare with the traditional Bonferroni adjustment
+#' p.adjust(c(pc,pd),method = "bonferroni")
 #' @export
 MixBonf.p.adjust <- function(pc, pd, pd.set, alpha = 0.05, make.decision = FALSE){
   mc <- length(pc); md <- length(pd); m <- mc+md

R/FWERSU.R

@@ -13,14 +13,21 @@
 #' @seealso \code{\link{Roth.p.adjust}},  \code{\link[stats]{p.adjust}}.
 #' @author Yalin Zhu
 #' @references
+#' Zhu, Y., & Guo, W. (2017).
+#' Familywise error rate controlling procedures for discrete data
+#' \emph{arXiv preprint} arXiv:1711.08147.
+#'
 #'  Hochberg, Y. (1988).
 #'  A sharper Bonferroni procedure for multiple tests of significance.
 #'  \emph{ Biometrika}, \strong{75}: 800-803.
-#'
 #' @examples
 #' p <- c(pbinom(1,8,0.5),pbinom(1,5,0.75),pbinom(1,6,0.6))
 #' p.set <-list(pbinom(0:8,8,0.5),pbinom(0:5,5,0.75),pbinom(0:6,6,0.6))
 #' MHoch.p.adjust(p,p.set)
+#' ## Compare with the traditional Hochberg adjustment
+#' p.adjust(p,method = "hochberg")
+#' ## Compare with the Roth adjustment
+#' Roth.p.adjust(p,p.set)
 #' @export
 
 MHoch.p.adjust <- function(p,p.set, alpha = 0.05, make.decision = FALSE){
@@ -61,7 +68,6 @@ MHoch.p.adjust <- function(p,p.set, alpha = 0.05, make.decision = FALSE){
 #' Hochberg, Y. (1988).
 #'  A sharper Bonferroni procedure for multiple tests of significance.
 #'  \emph{ Biometrika}, \strong{75}: 800-803.
-#'
 #' @examples
 #' p <- c(pbinom(1,8,0.5),pbinom(1,5,0.75),pbinom(1,6,0.6))
 #' p.set <-list(pbinom(0:8,8,0.5),pbinom(0:5,5,0.75),pbinom(0:6,6,0.6))

R/MHTdiscrete.R

@@ -2,7 +2,7 @@
 #'
 #' The MHTdiscrete package provides two categories of important functions for discrete data mutliple hypothese testing:
 #' @section FWER controlling procedures:
-#'        Single-step: MBonf.p.adjust, Tarone.p.adjust.
+#'        Single-step: MBonf.p.adjust, MixBonf.p.adjust, Tarone.p.adjust.
 #'
 #'        Step-down: MHolm.p.adjust, TH.p.adjust.
 #'

R/Pval.R

@@ -5,12 +5,16 @@
 #' @usage
 #' getPval(raw.data, test.type, alternative)
 #' @param raw.data original data set with count number for treatment group and study group. The data set type could be \code{\link[base]{matrix}} or \code{\link[base]{data.frame}}.
-#' @param test.type there are two discrete test available now, must be one of \code{"FET"} for Fisher's Exact Test and \code{"EBT"} for Exact Binomial Test.
+#' @param test.type there are two discrete test available now, must be one of \code{"FET"} for Fisher's Exact Test and \code{"BET"} for Binomial Exact Test.
 #' @param alternative indicates the alternative hypothesis and must be one of \code{"two.sided"}, \code{"greater"} or \code{"less"}.
 #' @return
 #' A numeric vector of the adjusted p-values (of the same length as \eqn{p}).
 #' @author Yalin Zhu
 #' @references
+#' Zhu, Y., & Guo, W. (2017).
+#' Familywise error rate controlling procedures for discrete data
+#' \emph{arXiv preprint} arXiv:1711.08147.
+#'
 #' Clopper, C. J. & Pearson, E. S. (1934).
 #' The use of confidence or fiducial limits illustrated in the case of the binomial.
 #' \emph{Biometrika}, \strong{26}: 404-413.
@@ -32,7 +36,7 @@
 #' p.set <- getPval(raw.data=df, test.type = "FET",alternative = "two.sided")[[2]]; p.set
 #' @export
 
-getPval <- function(raw.data, test.type = c("FET", "EBT"), alternative = c("two.sided","greater","less")){
+getPval <- function(raw.data, test.type = c("FET", "BET"), alternative = c("two.sided","greater","less")){
   if (class(raw.data)!="data.frame" & class(raw.data)!="matrix"){
     stop("The raw data type has to be 'data.frame' or 'matrix'")
   }
@@ -50,7 +54,7 @@ getPval <- function(raw.data, test.type = c("FET", "EBT"), alternative = c("two.
     p.set[[i]] <- sort(s)
   }
  }
- if (test.type=="EBT"){
+ if (test.type=="BET"){
    for (i in 1:nrow(raw.data)){
      p[i] <- stats::binom.test(c(raw.data[i,1],raw.data[i,2]), p = 0.5, alternative = alternative)[[3]]
      s<-c()