#'Display a summary of the results of \code{cv_sparseSCA()}.
#'
#'@param object Object of class inheriting from 'CVsparseSCA'.
#'@param disp The default is \code{"tuning"}; in this case, the recommended tuning
#' parameter values are presented.
#' If \code{"estimatedPT"}, then the estimated component loading and estimated
#' component score matrices (based on the recommended tuning paramter
#' values) are presented.
#' If \code{"full"}, then information is displayed regarding 1) the
#' recommended tuning parameter values, 2) the estimated component
#' loading and estimated component score matrices (based on the
#' recommended tuning parameter values), 3) # of variable selected,
#' 4) Mean squared prediction error (MSPE),
#' 5) standard errors for MSPE, 6) Lasso and Group Lasso tuning
#' parameter values that have been evaluated.
#'@param ... Argument to be passed to or from other methods.
#'@examples
#'\dontrun{
#'## S3 method for class 'CVsparseSCA'
#'summary(object, disp="full")
#'}
#'
#'@export
summary.CVsparseSCA <- function(object, disp, ...){
if(missing(disp)){
disp <- "tuning"
}
if(disp == "tuning"){
cat(sprintf("\nRecommended tuning parameter values are:\n"))
print(object$RecommendedLambda)
} else if(disp == "estimatedPT"){
cat(sprintf("\nEstimated component loading matrix, given the recommended tuning parameter values are:\n"))
print(object$P_hat)
cat(sprintf("\nEstimated component score matrix, given the recommended tuning parameter values are:\n"))
print(object$T_hat)
}else if(disp == "full"){
cat(sprintf("\nRecommended tuning parameter values are:\n"))
print(object$RecommendedLambda)
cat(sprintf("\nEstimated component loading matrix, given the recommended tuning parameter values are:\n"))
print(object$P_hat)
cat(sprintf("\nEstimated component score matrix, given the recommended tuning parameter values are:\n"))
print(object$T_hat)
#cat(sprintf("\nGiven each value for Group Lasso tuning parameters, the proper region for Lasso tuning parameter values are:\n"))
#print(object$Lambdaregion)
cat(sprintf("\n# of variable selected:\n"))
print(object$VarSelected)
cat(sprintf("\nMean squared prediction error (MSPE):\n"))
print(object$MSPE)
cat(sprintf("\nstandard errors for MSPE:\n"))
print(object$SE_MSE)
cat(sprintf("\nLasso tuning parameter values that have been evaluated:\n"))
print(object$Lasso_values)
cat(sprintf("\nGroup Lasso tuning parameter values that have been evaluated:\n"))
print(object$Glasso_values)
}else{
stop("either simple or full")
}
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.