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#'Display a summary of the results of \code{cv_structuredSCA()}.
#'
#'@param object Object of class inheriting from 'CVstructuredSCA'.
#'@param disp The default is \code{"tuning"}; in this case, the recommended tuning
#' parameter values for Lasso is displayed
#' If \code{"estimatedPT"}, then the estimated component loading and
#' component score matrices (given the recommended tuning parameter)
#' is displayed.
#' If \code{"full"}, then information is displayed regarding 1) the
#' recommended tuning parameter values for Lasso, 2) the estimated component
#' loading and component score matrices, 3) the proper region
#' for Lasso tuning parameter values, based on the 1SE rule, 4) mean squared
#' prediction error (MSPE), 5) Lasso tuning
#' parameter values that have been evaluated.
#'@param ... Argument to be passed to or from other methods.
#'@examples
#'\dontrun{
#'## S3 method for class 'CVstructuredSCA'
#'summary(object, disp="full")
#'}
#'
#'@export
summary.CVstructuredSCA <- function(object, disp, ...){
PRESS <- object$MSPE
LassoSequence <- object$LassoSequence
LassoRegion <- object$LassoRegion
RecommendedLasso <- object$RecommendedLasso
if(missing(disp)){
disp <- "tuning"
}
if(disp == "tuning"){
cat(sprintf("\nRecommended tuning parameter value for Lasso:\n"))
print(RecommendedLasso)
}else if(disp == "estimatedPT"){
cat(sprintf("\nEstimated component loading matrix, given the recommended Lasso tuning parameter:\n"))
print(object$P_hat)
cat(sprintf("\nEstimated component score matrix, given the recommended Lasso tuning parameter:\n"))
print(object$T_hat)
}else if(disp == "full"){
cat(sprintf("\nRecommended tuning parameter value for Lasso:\n"))
print(RecommendedLasso)
cat(sprintf("\nEstimated component loading matrix, given the recommended Lasso tuning parameter:\n"))
print(object$P_hat)
cat(sprintf("\nEstimated component score matrix, given the recommended Lasso tuning parameter:\n"))
print(object$T_hat)
cat(sprintf("\nA region for suitable Lasso tuning parameter values based on 1SE rule:\n"))
print(LassoRegion)
cat(sprintf("\nMean squared prediction error (MSPE):\n"))
print(PRESS)
cat(sprintf("\nLasso tuning parameter values that have been evaluated:\n"))
print(LassoSequence)
}else{
stop("either simple or full")
}
}
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