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#' Sakai Sequential Relation Analysis Print
#'
#' \code{print} function for the \code{ssra} object
#'
#' Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) are graphical approaches
#'
#' @param x requires the result object of hssr function
#' @param digits integer indicating the number of decimal places to be used
#' @param ... further arguments passed to or from other methods
#'
#' @author
#' Takuya Yanagida
#' Keiko Sakai
#'
#' @seealso
#' \code{\link{seqtable}}
#'
#' @references
#' Takeya, M. (1991). \emph{A new test theory: Structural analyses for educational information}.
#' Tokyo: Waseda University Press.
#'
#' @export
#'
#' @examples
#' # Example data based on Takeya (1991)
#'
#' # Sakai Sequential Relation Analysis
#' # ordering assesed according to the correlation coefficient and mean difference
#' exdat.ssra <- SSRA(exdat, output = FALSE)
#' print(exdat.ssra)
print.ssra <- function(x, digits = 3, ...) {
#--------------------------------------------------------------------------------------------------------------------#
dat <- x$dat
# Check for missing values
if (any(is.na(dat))) {
if (x$args$pairwise == TRUE) {
warning("Data matrix contains missing values. Pairwise deletion applied for missing values")
} else {
# Listwise deletion
dat <- na.omit(dat)
warning("Data matrix contains missing values. Listwise deletion applied for missing values")
}
}
cat("--------------------------------------------------------------------------\n")
cat(" Call: "); print(x$call)
cat(" Time: ", x$time, "\n")
cat(" R: ", x$R, "\n")
cat(" Package:", x$package, "\n")
cat("--------------------------------------------------------------------------\n\n")
###
cat("\n Sakai Sequential Relation Analysis", "\n\n ",
ifelse(x$args$method == "pearson", "Product-moment correlation coefficient",
ifelse(x$args$method == "spearman", "Spearman correlation", "Kendall-Tau")), "\n",
" Correlation coefficient criterion to be judged 'sequential' or 'equivalent:", x$args$r.crt, "\n\n",
" Paired t-test", "\n",
" Absolute mean difference criterion to be judged 'sequential':", x$args$mu.sq, "\n",
" Maximal absolute mean difference to be judged 'equivalent':", x$args$mu.eq, "\n",
" Effect size for mean difference criterion to be judged 'sequential':", x$args$d.sq, "\n",
" Maximal effect size for mean difference to be judged 'equivalent':", x$args$d.eq, "\n\n",
" Significance level:", x$args$alpha * 100, "%", "\n")
if (x$args$p.adjust.method != "none") {
cat(" Correction method for multiple comparisons:", paste0(toupper(substr(x$args$p.adjust.method, 1, 1)),
substr(x$args$p.adjust.method, 2, nchar(x$args$p.adjust.method))), "\n\n")
} else {
cat(" No alpha protection for multiple testing", "\n\n")
}
#------------------------------------------------#
# Create object result.d
result <- x$restab
#------------------------------------------------#
# Object result.d
result.d <- data.frame(result[, c("j", "k", "n")],
round(result[, c("j.mean", "j.sd", "k.mean", "k.sd", "r", "r.t")], digits = digits),
r.p = formatC(result[, "r.p"], format = "f", digits = 3),
r.sig = result[, "r.sig"], r.crt = result[, "r.crt"],
round(result[, c("m.diff", "sd.diff", "m.diff.eff", "m.diff.t")], digits = digits),
m.diff.p = formatC(result[, "m.diff.p"], format = "f", digits = 3),
m.diff.sig = result[, "m.diff.sig"],
result[, c("m.diff.crt.sq", "m.diff.crt.eq")],
result[, c("seq", "eq", "order")],
stringsAsFactors = FALSE)
# Exclude paths with no relationship
if (x$args$exclude == TRUE) {
result.d <- result.d[result.d$order != "", ]
row.names(result.d) <- NULL
}
print(result.d)
}
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