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@@ -9,13 +9,20 @@ Suggests:
 Author: Yalin Zhu, Wenge Guo
 Maintainer: Yalin Zhu <[email protected]>
 BugReports: https://github.com/allenzhuaz/FixSeqMTP/issues
-Description: Several generalized/directional Fixed Sequence Multiple Testing Procedures (g-
-    FSMTPs/d-FSMTPs) are developed for testing a sequence of pre-ordered hypotheses while
-    controlling the FWER, FDR and Directional Error (mdFWER). All three FWER controlling g-FSMTPs are designed under arbitrary dependence, which allow any number of acceptances. Two FDR controlling g-FSMTPs are respectively designed under arbitrary dependence and independence, which allow more but a given number of acceptances. Two mdFWER controlling d-FSMTPs are respectively designed under arbitrary dependence and independence, which can also make directional decisions based on the signs of the test statistics. The main
-    functions for each proposed g-FSMTPs are designed to calculate adjusted p-values
-    and critical values, respectively. For users' convenience, the output results
-    also include the option of decision rules for convenience.
+Description: Several generalized / directional Fixed Sequence Multiple Testing
+    Procedures (FSMTPs) are developed for testing a sequence of pre-ordered
+    hypotheses while controlling the FWER, FDR and Directional Error (mdFWER).
+    All three FWER controlling generalized FSMTPs are designed under arbitrary
+    dependence, which allow any number of acceptances. Two FDR controlling
+    generalized FSMTPs are respectively designed under arbitrary dependence and
+    independence, which allow more but a given number of acceptances. Two mdFWER
+    controlling directional FSMTPs are respectively designed under arbitrary
+    dependence and independence, which can also make directional decisions based
+    on the signs of the test statistics. The main functions for each proposed
+    generalized / directional FSMTPs are designed to calculate adjusted p-values
+    and critical values, respectively. For users' convenience, the functions also
+    provide the output option for printing decision rules.
 License: GPL (>= 2)
 Encoding: UTF-8
 LazyData: TRUE
-RoxygenNote: 5.0.1
+

FixSeqMTP.Rproj

@@ -0,0 +1,21 @@
+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

R/FSmdFWER.R

@@ -1,4 +1,4 @@
-#' Critical values for Fixed Sequence mdFWER Controlling Procedure under Arbitrary Dependence
+#' Critical values for Fixed Sequence mdFWER Controlling Procedure under Arbitrary Dependence Along with Directional Decisions Regarding Parameters of Interest
 #'
 #' Given a set of pre-ordered test statistics and the corresponding p-values, returns critical values using the directional fixed sequence multiple testing procedures under arbitrary dependence (See Procedure 1 and Theorem 1 in Grandhi et al. (2016)). The function also provides an option to make decisions and determine the sign given a pre-specified significant level \eqn{\alpha} and the test statistics.
 #'
@@ -20,7 +20,6 @@
 #' ## Clinical trial example in Grandhi et al. (2016)
 #' Pval <- c(0.0008, 0.0135, 0.0197, 0.7237, 0.0003, 0.2779, 0.0054, 0.8473)
 #' Zstat <- c(3.4434, 2.5085, 2.3642, -0.3543, 3.7651, 1.0900, 2.8340, 0.1930)
-#' FSmdFWER.arbidept.cv(p = Pval, make.decision = FALSE)
 #' FSmdFWER.arbidept.cv(p = Pval, test.stat = Zstat, make.decision = TRUE)
 #' @export
 FSmdFWER.arbidept.cv <- function(p, test.stat, alpha=0.05, make.decision = TRUE){
@@ -43,7 +42,7 @@ FSmdFWER.arbidept.cv <- function(p, test.stat, alpha=0.05, make.decision = TRUE)
 }
 
 
-#' Adjusted P-values for Fixed Sequence mdFWER Controlling Procedure under Arbitrary Dependence
+#' Adjusted P-values for Fixed Sequence mdFWER Controlling Procedure under Arbitrary Dependence Along with Directional Decisions Regarding Parameters of Interest
 #'
 #' Given a set of pre-ordered test statistics and the corresponding p-values, returns adjusted p-values using the directional fixed sequence multiple testing procedures under arbitrary dependence (See Procedure 1 and Theorem 1 in Grandhi et al. (2016)). The function also provides an option to make decisions and determine the sign given a pre-specified significant level \eqn{\alpha} and the test statistics.
 #'
@@ -65,7 +64,6 @@ FSmdFWER.arbidept.cv <- function(p, test.stat, alpha=0.05, make.decision = TRUE)
 #' ## Clinical trial example in Grandhi et al. (2016)
 #' Pval <- c(0.0008, 0.0135, 0.0197, 0.7237, 0.0003, 0.2779, 0.0054, 0.8473)
 #' Zstat <- c(3.4434, 2.5085, 2.3642, -0.3543, 3.7651, 1.0900, 2.8340, 0.1930)
-#' FSmdFWER.arbidept.p.adjust(p = Pval, make.decision = FALSE)
 #' FSmdFWER.arbidept.p.adjust(p = Pval, test.stat = Zstat, make.decision = TRUE)
 #' @export
 FSmdFWER.arbidept.p.adjust <- function(p, test.stat, alpha=0.05, make.decision = TRUE){
@@ -82,7 +80,7 @@ FSmdFWER.arbidept.p.adjust <- function(p, test.stat, alpha=0.05, make.decision =
 
 
 
-#' Critical values for Fixed Sequence mdFWER Controlling Procedure under Independence
+#' Critical values for Fixed Sequence mdFWER Controlling Procedure under Independence Along with Directional Decisions Regarding Parameters of Interest
 #'
 #' Given a set of pre-ordered test statistics and the corresponding p-values, returns critical values using the directional fixed sequence multiple testing procedures under independence (See Procedure 2 and Theorem 2 in Grandhi et al. (2016)). The function also provides an option to make decisions and determine the sign given a pre-specified significant level \eqn{\alpha} and the test statistics.
 #'
@@ -104,7 +102,6 @@ FSmdFWER.arbidept.p.adjust <- function(p, test.stat, alpha=0.05, make.decision =
 #' ## Clinical trial example in Grandhi et al. (2016)
 #' Pval <- c(0.0008, 0.0135, 0.0197, 0.7237, 0.0003, 0.2779, 0.0054, 0.8473)
 #' Zstat <- c(3.4434, 2.5085, 2.3642, -0.3543, 3.7651, 1.0900, 2.8340, 0.1930)
-#' FSmdFWER.indept.cv(p = Pval, make.decision = FALSE)
 #' FSmdFWER.indept.cv(p = Pval, test.stat = Zstat, make.decision = TRUE)
 #' @export
 FSmdFWER.indept.cv <- function(p, test.stat, alpha=0.05, make.decision = TRUE){
@@ -126,7 +123,7 @@ FSmdFWER.indept.cv <- function(p, test.stat, alpha=0.05, make.decision = TRUE){
   } else{return(cv)}
 }
 
-#' Adjusted P-values for Fixed Sequence mdFWER Controlling Procedure under Independence
+#' Adjusted P-values for Fixed Sequence mdFWER Controlling Procedure under Independence Along with Directional Decisions Regarding Parameters of Interest
 #'
 #' Given a set of pre-ordered test statistics and the corresponding p-values, returns adjusted p-values using the directional fixed sequence multiple testing procedures under independence (See Procedure 2 and Theorem 2 in Grandhi et al. (2016)). The function also provides an option to make decisions and determine the sign given a pre-specified significant level \eqn{\alpha} and the test statistics.
 #'
@@ -148,7 +145,6 @@ FSmdFWER.indept.cv <- function(p, test.stat, alpha=0.05, make.decision = TRUE){
 #' ## Clinical trial example in Grandhi et al. (2015)
 #' Pval <- c(0.0008, 0.0135, 0.0197, 0.7237, 0.0003, 0.2779, 0.0054, 0.8473)
 #' Zstat <- c(3.4434, 2.5085, 2.3642, -0.3543, 3.7651, 1.0900, 2.8340, 0.1930)
-#' FSmdFWER.indept.p.adjust(p = Pval, make.decision = FALSE)
 #' FSmdFWER.indept.p.adjust(p = Pval, test.stat = Zstat, make.decision = TRUE)
 #' @export
 FSmdFWER.indept.p.adjust <- function(p, test.stat, alpha=0.05, make.decision = TRUE){