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DESCRIPTION

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+Package: MHTdiscrete
+Type: Package
+Title: Multiple Hypotheses Testing for Discrete Data
+Version: 0.1.3
+Suggests: multcomp, FixSeqMTP
+Author: Yalin Zhu, Wenge Guo
+Maintainer: Yalin Zhu <yalin.zhu@outlook.com>
+BugReports: https://github.com/allenzhuaz/MHTdiscrete/issues
+URL: https://allen.shinyapps.io/MTPs/
+Description: A Comprehensive tool for almost all existing multiple testing
+    methods for discrete data. The package also provides some novel multiple testing
+    procedures controlling FWER/FDR for discrete data. Given discrete p-values
+    and their domains, the [method].p.adjust function returns adjusted p-values,
+    which can be used to compare with the nominal significant level alpha and make
+    decisions. For users' convenience, the functions also provide the output option 
+    for printing decision rules.
+License: GPL (>= 2)
+LazyData: TRUE
+NeedsCompilation: yes

MHTdiscrete.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source
+PackageRoxygenize: rd,collate,namespace

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(GTBH.p.adjust)
+export(GTBY.p.adjust)
+export(MBH.p.adjust)
+export(MBL.p.adjust)
+export(MBY.p.adjust)
+export(MBonf.p.adjust)
+export(MHoch.p.adjust)
+export(MHolm.p.adjust)
+export(MixBonf.p.adjust)
+export(Roth.p.adjust)
+export(Roth.rej)
+export(Sidak.p.adjust)
+export(TH.p.adjust)
+export(Tarone.p.adjust)
+export(getPval)

R/FDRSD.R

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+#' The adjusted p-values for Modified Benjamini-Liu (BL) step-down FDR controlling procedure.
+#'
+#' The function for calculating the adjusted p-values based on original available p-values and all attaianble p-values.
+#'
+#' @usage
+#' MBL.p.adjust(p, p.set, alpha, make.decision)
+#' @param p numeric vector of p-values (possibly with \code{\link[base]{NA}}s). Any other R is coerced by \code{\link[base]{as.numeric}}. Same as in \code{\link[stats]{p.adjust}}.
+#' @param p.set a list of numeric vectors, where each vector is the vector of all attainable p-values containing the available p-value for the corresponding hypothesis.
+#' @param alpha significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.
+#' @param make.decision logical; if  \code{TRUE}, then the output include the decision rules compared adjusted p-values with significant level \eqn{\alpha}
+#' @return
+#' A numeric vector of the adjusted p-values (of the same length as \code{p}).
+#' @seealso \code{\link{MBH.p.adjust}},  \code{\link{MBY.p.adjust}}
+#' @author Yalin Zhu
+#' @references
+#' Benjamini, Y., and Liu, W. (1999).
+#' A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence.
+#'  \emph{Journal of Statistical Planning and Inference}, \strong{82}: 163-170.
+#' @note The MBL procedure for discrete data controls FDR under the specific dependence assumption where the joint distribution of statistics from true nulls are independent of the joint distribution of statistics from false nulls.
+#' @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))
+#' MBL.p.adjust(p,p.set)
+#' @export
+
+MBL.p.adjust <- function(p,p.set, alpha = 0.05, make.decision = FALSE){
+  o <- order(p); ro <- order(o); m <- length(p)
+  sort.p <- p[o]; sort.p.set <- p.set[o]
+  adjP <- numeric(m); pCDF <- matrix(NA,m,m)
+  for(i in 1:m){
+    for(j in i:m){
+      pCDF[i,j] <- max(sort.p.set[[j]][sort.p.set[[j]] <= sort.p[i]],0)
+    }
+    c <- (m-i+1)/m*(1-prod(1-pCDF[i,i:m]))
+    adjP[i] <- ifelse(i==1,c,max(adjP[i-1],c))
+  }
+  if (make.decision==FALSE){
+    return(adjP[ro])
+  } else{
+    return(list(method= "Modified Benjamini-Liu (BL)", significant.level = alpha, Result = data.frame(raw.p = p, adjust.p=adjP[ro], decision=ifelse(adjP[ro]<=alpha, "reject","accept"))))
+  }
+}

R/FDRSU.R

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+#' The adjusted p-values for Modified Benjamini-Hochberg (BH) step-up FDR controlling procedure.
+#'
+#' The function for calculating the adjusted p-values based on original available p-values and all attaianble p-values.
+#'
+#' @usage
+#' MBH.p.adjust(p, p.set, alpha, make.decision)
+#' @param p numeric vector of p-values (possibly with \code{\link[base]{NA}}s). Any other R is coerced by \code{\link[base]{as.numeric}}. Same as in \code{\link[stats]{p.adjust}}.
+#' @param p.set a list of numeric vectors, where each vector is the vector of all attainable p-values containing the available p-value for the corresponding hypothesis.
+#' @param alpha significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.
+#' @param make.decision logical; if  \code{TRUE}, then the output include the decision rules compared adjusted p-values with significant level \eqn{\alpha}
+#' @return
+#' A numeric vector of the adjusted p-values (of the same length as \code{p}).
+#' @seealso \code{\link{MBY.p.adjust}},  \code{\link{MBL.p.adjust}}
+#' @author Yalin Zhu
+#' @references
+#' Benjamini, Y., and Hochberg, Y. (1995).
+#'  Controlling the false discovery rate: a practical and powerful approach to multiple testing.
+#'  \emph{Journal of the Royal Statistical Society Series B}, \strong{57}: 289-300.
+#' @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))
+#' MBH.p.adjust(p,p.set)
+#' @export
+
+MBH.p.adjust <- function(p,p.set, alpha = 0.05, make.decision = FALSE){
+  o <- order(p); ro <- order(o); m <- length(p)
+  sort.p <- p[o]; sort.p.set <- p.set[o]
+  adjP <- numeric();pCDF <- matrix(NA,m,m)
+  for(i in m:1){
+    for(j in 1:m){
+      pCDF[i,j] <- max(sort.p.set[[j]][sort.p.set[[j]] <= sort.p[i]],0)
+    }
+    c <- sum(pCDF[i,1:m])/i
+    adjP[i] <- ifelse(i==m,c,min(adjP[i+1],c))
+  }
+  if (make.decision==FALSE){
+    return(adjP[ro])
+  } else{
+    return(list(method= "Modified Benjamini-Hochberg (BH)", significant.level = alpha, Result = data.frame(raw.p = p, adjust.p=adjP[ro], decision=ifelse(adjP[ro]<=alpha, "reject","accept"))))
+  }
+}
+
+#' The adjusted p-values for Gilbert-Tarone-BH step-up FDR controlling procedure.
+#'
+#' The function for calculating the adjusted p-values based on original available p-values and all attaianble p-values.
+#'
+#' @usage
+#' GTBH.p.adjust(p, p.set, alpha, make.decision)
+#' @param p numeric vector of p-values (possibly with \code{\link[base]{NA}}s). Any other R is coerced by \code{\link[base]{as.numeric}}. Same as in \code{\link[stats]{p.adjust}}.
+#' @param p.set a list of numeric vectors, where each vector is the vector of all attainable p-values containing the available p-value for the corresponding hypothesis.
+#' @param alpha significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.
+#' @param make.decision logical; if  \code{TRUE}, then the output include the decision rules compared adjusted p-values with significant level \eqn{\alpha}
+#' @return
+#' A numeric vector of the adjusted p-values (of the same length as \code{p}).
+#' @seealso \code{\link{GTBY.p.adjust}},  \code{\link{MBH.p.adjust}},  \code{\link{MBY.p.adjust}}
+#' @author Yalin Zhu
+#' @references
+#' Gilbert, P. B. (2005).
+#' A modified false discovery rate multiple-comparisons procedure for discrete data, applied to human immunodeficiency virus genetics.
+#' \emph{Journal of the Royal Statistical Society: Series C (Applied Statistics)}, \strong{54}: 143-158.
+#'
+#' Benjamini, Y., and Hochberg, Y. (1995).
+#'  Controlling the false discovery rate: a practical and powerful approach to multiple testing.
+#'  \emph{Journal of the Royal Statistical Society Series B}, \strong{57}: 289-300.
+#'
+#' @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))
+#' GTBH.p.adjust(p,p.set)
+#' @export
+
+GTBH.p.adjust <- function(p,p.set, alpha = 0.05, make.decision = FALSE){
+  o <- order(p); ro <- order(o); m <- length(p); adjP <- numeric(m)
+  sort.p <- p[o]; sort.p.set <- p.set[o]
+  minP <- sort(sapply(sort.p.set,min))
+  for (j in m:1){
+    for (i in 1:m ){
+      if(sort.p[j]>=max(minP)){q <- m}
+      else if (sort.p[j]>=minP[i] & sort.p[j]<minP[i+1]){q <- i}
+    }
+    adjP[j] <-  ifelse(j==q,sort.p[j],min(adjP[j+1],q*sort.p[j]/j))
+  }
+  if (make.decision==FALSE){
+    return(adjP[ro])
+  } else{
+    return(list(method= "Gilbert-Tarone-BH", significant.level = alpha, Result = data.frame(raw.p = p, adjust.p=adjP[ro], decision=ifelse(adjP[ro]<=alpha, "reject","accept"))))
+  }
+}
+
+
+
+#' The adjusted p-values for Modified Benjamini-Yekutieli (BY) step-up FDR controlling procedure.
+#'
+#' The function for calculating the adjusted p-values based on original available p-values and all attaianble p-values.
+#'
+#' @usage
+#' MBY.p.adjust(p, p.set, alpha, make.decision)
+#' @param p numeric vector of p-values (possibly with \code{\link[base]{NA}}s). Any other R is coerced by \code{\link[base]{as.numeric}}. Same as in \code{\link[stats]{p.adjust}}.
+#' @param p.set a list of numeric vectors, where each vector is the vector of all attainable p-values containing the available p-value for the corresponding hypothesis.
+#' @param alpha significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.
+#' @param make.decision logical; if  \code{TRUE}, then the output include the decision rules compared adjusted p-values with significant level \eqn{\alpha}
+#' @return
+#' A numeric vector of the adjusted p-values (of the same length as \code{p}).
+#' @seealso  \code{\link{MBH.p.adjust}},  \code{\link{MBL.p.adjust}}
+#' @author Yalin Zhu
+#' @references
+#' Benjamini, Y., and Yekutieli, D. (2001).
+#' The control of the false discovery rate in multiple testing under dependency.
+#' \emph{Annals of Statistics}, \strong{29}: 1165-1188.
+#' @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))
+#' MBY.p.adjust(p,p.set)
+#' @export
+
+
+MBY.p.adjust <- function(p,p.set, alpha = 0.05, make.decision = FALSE){
+  o <- order(p); ro <- order(o); m <- length(p); C <- sum(1/c(1:m))
+  sort.p <- p[o]; sort.p.set <- p.set[o]
+  adjP <- numeric();pCDF <- matrix(NA,m,m)
+  for(i in m:1){
+    for(j in 1:m){
+      pCDF[i,j] <- max(sort.p.set[[j]][sort.p.set[[j]] <= sort.p[i]],0)
+    }
+    c <- min(1,sum(pCDF[i,1:m])*C/i)
+    adjP[i] <- ifelse(i==m,c,min(adjP[i+1],c))
+  }
+  if (make.decision==FALSE){
+    return(adjP[ro])
+  } else{
+    return(list(method= "Modified Benjamini-Yekutieli (BY)", significant.level = alpha, Result = data.frame(raw.p = p, adjust.p=adjP[ro], decision=ifelse(adjP[ro]<=alpha, "reject","accept"))))
+  }
+}
+
+
+#' The adjusted p-values for Gilbert-Tarone-BY step-up FDR controlling procedure.
+#'
+#' The function for calculating the adjusted p-values based on original available p-values and all attaianble p-values.
+#'
+#' @usage
+#' GTBY.p.adjust(p, p.set, alpha, make.decision)
+#' @param p numeric vector of p-values (possibly with \code{\link[base]{NA}}s). Any other R is coerced by \code{\link[base]{as.numeric}}. Same as in \code{\link[stats]{p.adjust}}.
+#' @param p.set a list of numeric vectors, where each vector is the vector of all attainable p-values containing the available p-value for the corresponding hypothesis.
+#' @param alpha significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.
+#' @param make.decision logical; if  \code{TRUE}, then the output include the decision rules compared adjusted p-values with significant level \eqn{\alpha}
+#' @return
+#' A numeric vector of the adjusted p-values (of the same length as \code{p}).
+#' @seealso \code{\link{GTBH.p.adjust}},  \code{\link{MBH.p.adjust}},  \code{\link{MBY.p.adjust}}
+#' @author Yalin Zhu
+#' @references
+#' Gilbert, P. B. (2005).
+#' A modified false discovery rate multiple-comparisons procedure for discrete data, applied to human immunodeficiency virus genetics.
+#' \emph{Journal of the Royal Statistical Society: Series C (Applied Statistics)}, \strong{54}: 143-158.
+#'
+#' Benjamini, Y., and Yekutieli, D. (2001).
+#' The control of the false discovery rate in multiple testing under dependency.
+#' \emph{Annals of Statistics}, \strong{29}: 1165-1188.
+#'
+#' @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))
+#' GTBY.p.adjust(p,p.set)
+#' @export
+
+GTBY.p.adjust <-  function(p,p.set, alpha = 0.05, make.decision = FALSE){
+  o <- order(p); ro <- order(o); m <- length(p); adjP <- numeric(m)
+  sort.p <- p[o]; sort.p.set <- p.set[o]
+  minP <- sort(sapply(sort.p.set,min))
+  for (j in m:1){
+    for (i in 1:m ){
+      if(sort.p[j]>=max(minP)){q <- m}
+      else if (sort.p[j]>=minP[i] & sort.p[j]<minP[i+1]){q <- i}
+    }
+    C <- sum(1/c(1:q))
+    adjP[j] <-  ifelse(j==q,sort.p[j],min(adjP[j+1],q*C*sort.p[j]/j))
+  }
+  if (make.decision==FALSE){
+    return(adjP[ro])
+  } else{
+    return(list(method= "Gilbert-Tarone-BY", significant.level = alpha, Result = data.frame(raw.p = p, adjust.p=adjP[ro], decision=ifelse(adjP[ro]<=alpha, "reject","accept"))))
+  }
+}

R/FWERSD.R

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+#' The adjusted p-values for Modified Holm step-down FWER controlling procedure.
+#'
+#' The function for calculating the adjusted p-values based on original available p-values and all attaianble p-values.
+#'
+#' @usage
+#' MHolm.p.adjust(p, p.set, alpha, make.decision)
+#' @param p numeric vector of p-values (possibly with \code{\link[base]{NA}}s). Any other R is coerced by \code{\link[base]{as.numeric}}. Same as in \code{\link[stats]{p.adjust}}.
+#' @param p.set a list of numeric vectors, where each vector is the vector of all attainable p-values containing the available p-value for the corresponding hypothesis.
+#' @param alpha significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.
+#' @param make.decision logical; if  \code{TRUE}, then the output include the decision rules compared adjusted p-values with significant level \eqn{\alpha}
+#' @return
+#' A numeric vector of the adjusted p-values (of the same length as \code{p}).
+#' @seealso \code{\link{TH.p.adjust}}, \code{\link[stats]{p.adjust}}.
+#' @author Yalin Zhu
+#' @references
+#' 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)
+#' @export
+
+MHolm.p.adjust <- function(p,p.set, alpha = 0.05, make.decision = FALSE){
+  o <- order(p); ro <- order(o); m <- length(p)
+  sort.p <- p[o]; sort.p.set <- p.set[o]
+  adjP <- numeric(m); pCDF <- matrix(NA,m,m)
+  for(i in 1:m){
+    for (j in i:m){
+      pCDF[i,j] <- max(sort.p.set[[j]][sort.p.set[[j]] <= sort.p[i]],0)
+    }
+    c <- min(1,sum(pCDF[i,i:m]))
+    adjP[i] <- ifelse(i==1,c,max(adjP[i-1],c))
+  }
+  if (make.decision==FALSE){
+    return(adjP[ro])
+  } else{
+    return(list(method= "Modified Holm", significant.level = alpha, Result = data.frame(raw.p = p, adjust.p=adjP[ro], decision=ifelse(adjP[ro]<=alpha, "reject","accept"))))
+  }
+}
+
+#' The adjusted p-values for Tarone-Holm step-down FWER controlling procedure.
+#'
+#' The function for calculating the adjusted p-values based on original available p-values and all attaianble p-values.
+#'
+#' @usage
+#' TH.p.adjust(p, p.set, alpha, make.decision)
+#' @param p numeric vector of p-values (possibly with \code{\link[base]{NA}}s). Any other R is coerced by \code{\link[base]{as.numeric}}. Same as in \code{\link[stats]{p.adjust}}.
+#' @param p.set a list of numeric vectors, where each vector is the vector of all attainable p-values containing the available p-value for the corresponding hypothesis.
+#' @param alpha significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.
+#' @param make.decision logical; if  \code{TRUE}, then the output include the decision rules compared adjusted p-values with significant level \eqn{\alpha}
+#' @return
+#' A numeric vector of the adjusted p-values (of the same length as \code{p}).
+#' @seealso \code{\link{MHolm.p.adjust}}, \code{\link[stats]{p.adjust}}.
+#' @author Yalin Zhu
+#' @references
+#' Hommel, G., & Krummenauer, F. (1998).
+#' Improvements and modifications of Tarone's multiple test procedure for discrete data.
+#' \emph{Biometrics}, \strong{54}: 673-681.
+#'
+#' 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))
+#' TH.p.adjust(p,p.set)
+#' @export
+
+
+TH.p.adjust <- function(p,p.set, alpha = 0.05, make.decision = FALSE){
+  o <- order(p); ro <- order(o); m <- length(p)
+  sort.p <- p[o]; sort.p.set <- p.set[o]; adjP <- numeric(m)
+  j <- 1
+  while (j <= m){
+    minP <- sort(sapply(sort.p.set[j:m],min))
+    for (i in 1:(m-j+1)){
+      if (sort.p[j]>=max(minP)){q <- m-j+1}
+      else if (sort.p[j]>=minP[i] & sort.p[j]<minP[i+1]){q <- i}
+    }
+    c <- min(1,q*sort.p[j])
+    adjP[j] <- ifelse(j==1,c,max(adjP[j-1],c))
+    j <- j+1
+  }
+  if (make.decision==FALSE){
+    return(adjP[ro])
+  } else{
+    return(list(method= "Tarone-Holm", significant.level = alpha, Result = data.frame(raw.p = p, adjust.p=adjP[ro], decision=ifelse(adjP[ro]<=alpha, "reject","accept"))))
+  }
+}
+
+
+