first commit

.Rbuildignore

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+^.*\.Rproj$
+^\.Rproj\.user$

.gitignore

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+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
+SampleWMW.Rproj

DESCRIPTION

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+Package: WMWssp
+Type: Package
+Title: Wilcoxon-Mann-Whitney Sample Size Planning
+Version: 0.3.2
+Date: 2018-02-16
+Authors@R: c(
+    person("Arne C. Bathke", role = "aut"),
+    person("Edgar Brunner", role = "aut"),
+    person("Martin Happ", role = c("aut", "cre"), email = "[email protected]"),
+    person("Frank Konietschke", role = "aut")
+  )
+Author: Arne C. Bathke [aut], Edgar Brunner [aut], Martin Happ [aut, cre], Frank Konietschke [aut]
+Maintainer: Martin Happ <[email protected]>
+Description: Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test
+    that is needed for a given power and two sided type I error rate. The method works for metric data with and without
+    ties, count data, ordered categorical data, and even dichotomous data.
+    But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect.
+    For details, see Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS, Springer Verlag, to appear.
+Imports: rankFD
+Depends: R (>= 3.4.0)
+License: GPL-3
+LazyData: TRUE
+RoxygenNote: 6.0.1

NAMESPACE

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+export(WMWssp, WMWssp_minimize, WMWssp_noether, WMWssp_maximize)
+importFrom("stats", "qnorm", "optimize", "pnorm")
+importFrom("rankFD", "rank.two.samples")
+S3method(print, WMWssp)
+S3method(summary, WMWssp)

R/S3methods.R

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+####################################################################################################################################
+### Filename:    S3methods.R
+### Description: S3 methods for class 'WMWssp' returned from functions from file samplesize.R
+###
+###
+###
+###
+####################################################################################################################################
+
+print.WMWssp <- function(x, ...){
+
+  cat("Wilcoxon-Mann-Whitney Sample Size Calculation\n \n")
+  cat("Total sample size needed: ")
+  cat(x$N, paste("(n_1 = ", ceiling(x$N*x$t), ", n_2 = ", ceiling(x$N*(1-x$t)), ")", sep = ""))
+  cat("\n")
+
+  if(as.character(x$call) %in% c("WMWssp_minimize", "WMWssp_maximize") ) {
+    cat("Optimal allocation rate to the first group: ")
+    cat(x$t)
+    cat("\n")
+  } else {
+    cat("Allocation rate to the first group: ")
+    cat(x$t)
+    cat("\n")
+  }
+
+  if(x$simulation >= 0){
+    cat("Simulated Power: ")
+    cat(x$simulation)
+    cat("\n")
+  }
+
+  if(as.character(x$call) %in% c("WMWssp_maximize")){
+    cat("Maximal Power: ")
+    cat(x$power)
+    cat("\n")
+  }
+
+  cat("\nUse 'summary' for more details.")
+
+}
+
+
+summary.WMWssp <- function(object, ...){
+  cat("Wilcoxon-Mann-Whitney Sample Size Calculation\n \n")
+  cat("Summary\n")
+  cat("Call: ")
+  cat(as.character(object$call))
+  cat("\n")
+  cat("Type-I error (two-sided): ")
+  cat(object$alpha)
+  cat("\n")
+  if(as.character(object$call) != "WMWssp_maximize"){
+    cat("Power: ")
+    cat(object$power)
+    cat("\n")
+  }
+  if(as.character(object$call) == "WMWssp_maximize"){
+    cat("Sample size: ")
+    cat(object$N)
+    cat("\n")
+  }
+  cat("\n")
+
+  print(object$result)
+}

R/example_maximize.txt

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+# Prior information for the reference group
+x <- c(315,375,356,374,412,418,445,403,431,410,391,475,379)
+# generate data for treatment group based on a shift effect
+y <- x - 20
+
+#
+N <- 112
+
+# calculate optimal t
+ssp <- WMWssp_maximize(x, y, alpha = 0.05, N)
+summary(ssp)

R/example_minimize.txt

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+# Prior information for the reference group
+x <- c(315,375,356,374,412,418,445,403,431,410,391,475,379)
+# generate data for treatment group based on a shift effect
+y <- x - 20
+
+# calculate optimal t
+ssp <- WMWssp_minimize(x, y, alpha = 0.05, power = 0.8)
+summary(ssp)

R/example_noether.txt

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+# Prior information for the reference group
+x <- c(315,375,356,374,412,418,445,403,431,410,391,475,379)
+# generate data for treatment group based on a shift effect
+y <- x - 20
+# this data leads to a relative effect of p = 0.349
+
+# calculate sampe size for a balanced design
+ssp <- WMWssp_noether(alpha = 0.05, power = 0.8, t =1/2, p = 0.349)
+summary(ssp)

R/example_ssp.txt

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+# Prior information for the reference group
+x <- c(315,375,356,374,412,418,445,403,431,410,391,475,379)
+# generate data for treatment group based on a shift effect
+y <- x - 20
+
+# calculate sample size
+ssp <- WMWssp(x, y, alpha = 0.05, power = 0.8, t = 1/2)
+summary(ssp)