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#' Summary method for class CICA
#' @description Summarize a CICA analysis
#' @param object Object of the type produced by \code{\link{CICA}}
#' @param ... Additional arguments
#'
#' @return \code{summary.CICA} returns an overview of the estimated clustering of a \code{\link{CICA}} analysis
#' \item{PM}{Partitioning matrix}
#' \item{tab}{tabulation of the clustering}
#' \item{Loss}{Loss function value of the solution}
#'
#' @examples
#' \dontrun{
#' CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
#' E = 0.4, overlap = .25, externalscore = TRUE)
#'
#' multiple_output = CICA(DataList = CICA_data$X, nComp = 2:6, nClus = 1:5,
#' userGrid = NULL, RanStarts = 30, RatStarts = NULL, pseudo = c(0.1, 0.2),
#' pseudoFac = 2, userDef = NULL, scalevalue = 1000, center = TRUE,
#' maxiter = 100, verbose = TRUE, ctol = .000001)
#'
#' summary(multiple_output$Q_5_R_4)
#' }
#'
#'
#' @export
#'
#'
summary.CICA <- function(object, ...){
cat('Partitioning matrix P: \n' )
PB <- matrix(0, nrow = length(object$P), ncol = length(unique(object$P)))
for(i in 1:nrow(PB)){
PB[i, object$P[i]] <- 1
}
colnames(PB) <- paste('Cluster',sort(unique(object$P)))
rownames(PB) <- names(object$P)
cat('\n')
print(PB)
cat('\n')
cat('Tabulation of clustering: \n')
cat('\n')
tab <- table(object$P)
names(tab) <- paste('Cluster',sort(unique(object$P)))
print( tab )
cat('\n')
cat('Loss function value of optimal solution is: ', object$Loss,'\n')
# out <- list()
# out$PM <- PB
# out$tab <- tab
# out$loss <- object$Loss
#
# return(out)
}
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