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##' Summary of \code{\link{mctp.rm}}
##'
##' The function summary.mctp.rm produces a result summary of
##' \code{\link{mctp.rm}}. It can only be applied to objects of class
##' "mctp.rm".
##'
##' Since summary.mctp.rm is a S3 method it suffices to use summary(x) as long
##' as x is of class "mctp.rm". It will be interpreted as summary.mctp.rm(x).
##'
##' @param object An object of class "mctp.rm", i.e. the result when applying
##' \code{\link{mctp.rm}} to a dataset. Otherwise an error will occur.
##' @param ... Arguments to be passed to methods.
##' @return The function produces a summary of the result of
##' \code{\link{mctp.rm}} starting with some global information: alternative
##' hypothesis, estimation method, type of contrast, confidence level. This is
##' followed by: \item{Data.Info }{List of samples and sample sizes and
##' estimated effect per group.} \item{Contrast}{Contrast matrix.}
##' \item{Analysis }{Estimator: Estimated relative effect, Lower: Lower limit
##' of the simultaneous confidence interval, Upper: Upper limit of the
##' simultaneous confidence interval, Statistic: Teststatistic p.Value:
##' Adjusted p-values for the hypothesis by the choosen approximation method.
##' }
##' @note It is possible to create a graphical result of the multiple
##' comparison test procedure by using the function \code{\link{plot.mctp.rm}}.
##' @author Marius Placzek
##' @seealso For further information on the usage of mctp.rm, see
##' \code{\link{mctp.rm}}.
##' @references F. Konietschke, A.C. Bathke, L.A. Hothorn, E. Brunner: Testing
##' and estimation of purely nonparametric effects in repeated measures
##' designs. Computational Statistics and Data Analysis 54 (2010) 1895-1905.
##' @keywords print
##' @examples
##'
##' data(panic)
##' a<-mctp.rm(CGI~week, data=panic, type = "Dunnett",
##' alternative = "two.sided",
##' asy.method = "fisher", contrast.matrix = NULL)
##' summary(a)
##'
summary.mctp.rm <-
function(object,...)
{
cat("\n", "#----------------Nonparametric Multiple Comparisons for relative effects---------------#", "\n","\n",
"-", "Alternative Hypothesis : ", object$text.Output,"\n",
"-", "Estimation Method : Global Pseudo ranks","\n",
"-", "Type of Contrast", ":", object$input$type, "\n", "-", "Confidence Level:",
object$input$conf.level*100,"%", "\n", "-", "Method", ":", object$AsyMethod, "\n","\n",
"#--------------------------------------------------------------------------------------#","\n",
"\n")
cat( " #----Data Info-------------------------------------------------------------------------#","\n")
print(object$Data.Info)
cat("\n", "#----Contrast--------------------------------------------------------------------------#","\n")
print(object$Contrast)
cat("\n", "#----Analysis--------------------------------------------------------------------------#","\n")
print(object$Analysis)
cat("\n", "#----Overall---------------------------------------------------------------------------#","\n")
print(object$Overall)
if (object$input$correlation == TRUE){
cat("\n", "#----Covariance------------------------------------------------------------------------#","\n")
print(object$Covariance)
cat("\n", "#----Correlation-----------------------------------------------------------------------#","\n")
print(object$Correlation)
}
cat("\n", "#--------------------------------------------------------------------------------------#","\n")
}
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