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#' Adjusting raw p-values of a CTP
#'
#' Function that adjusts the raw p-values of the elementary hypotheses of a closed testing procedure.
#' The raw p-values are adjusted according to the closure principle.
#' The adjusted p-value is calculated as the maximum of the raw p-value from the current hypothesis in question and the raw p-values from
#' all subsequent hypotheses that contain the current hypothesis.
#'
#' @param ctp.struc
#' Object generated by \code{\link{IntersectHypotheses}}
#'
#' @param p.value Vector of raw p-values in the order of the hypotheses created by \code{\link{summary.ctp.str}}
#'
#' @param dataset.name
#' Character string naming the analysis dataset (optional - only for documentation purposes).
#'
#' @param factor.name
#' Character string naming the factor whose levels are compared (optional - only for documentation purposes).
#'
#' @param factor.levels
#' Vector of type "character" containing the levels of the treatment factor
#' (optional - only for documentation purposes).
#'
#' @param model
#' Model used in the analysis (optional - only for documentation purposes).
#'
#' @param test.name
#' Character string naming the statistical test applied.
#'
#' @return
#' An object of \code{oldClass = "ctp"} to be used for summarizing and plotting the results.
#'
#' @seealso
#' \code{\link{IntersectHypotheses}}, \code{\link{AnalyseCTP}}, \code{\link{Display}},
#' \code{\link{summary.ctp}}
#'
#' @examples
#'
#' Pairwise <- IntersectHypotheses(list(c(1,2), c(1,3), c(1,4), c(2,3), c(2,4), c(3,4)))
#' Display(Pairwise)
#' summary(Pairwise)
#'
#' # the vector of p-values calculated by another software
#'
#' p.val <- c(
#' 0.4374,
#' 0.6485,
#' 0.4103,
#' 0.2203,
#' 0.1302,
#' 0.6725,
#' 0.4704,
#' 0.3173,
#' 0.6762,
#' 0.7112,
#' 0.2866,
#' 0.3362,
#' 0.2871,
#' 0.4633)
#'
#' result <- Adjust_raw(ctp.struc=Pairwise, p.value=p.val)
#'
#' ## details may be documented
#'
#' result <- Adjust_raw(Pairwise, p.value=p.val
#' ,dataset.name="my Data", factor.name="Factor"
#' ,factor.levels=c("A","B","C","D"), model=y~Factor
#' ,test.name="my Test")
#'
#' summary(result)
#' Display(result)
#'
#'
#' @export
Adjust_raw <- function(ctp.struc, p.value, dataset.name = NULL, factor.name = NULL
,factor.levels = NULL, model = NULL, test.name = NULL)
{
hyplist <- ctp.struc$hypothesis
hypnames <- ctp.struc$hypnames
connections <- ctp.struc$connections
len <- dim(hypnames)[1]
if(!is.numeric(p.value))
stop("vector of p-values must be numeric")
if(sum(is.na(p.value)) > 0)
stop("Some p-values are missing (NA's)")
if(length(p.value) != len)
stop("Number of p-values must be equal to number of hypotheses")
hypnames$pvalue <- p.value
test.name <- paste("ctp",test.name,sep=".")
pvadj <- Adjust_p(ctp.struc = ctp.struc, ctp.pval = hypnames)
CTPparms <- list(hyplist=hyplist,hypnames=hypnames,connections=connections
,model=model,data=NULL,test=test.name,fac=NULL,facname=factor.name
,level=factor.levels,nlevel=length(factor.levels),resp=NULL,respname="")
ctp.res <- list(CTPparms=CTPparms, pvalues = pvadj, info = NULL)
oldClass(ctp.res) <- "ctp"
ctp.res
}
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