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#' print attribute.profile.class
#'
#' @param x \code{\link{attribute.profile.class}} input object
#' @method print attribute.profile.class
#' @export
setMethod(
f = "print",
signature = signature(x = 'attribute.profile.class'),
definition = function(x)
{
print(x@results)
}
)
#' head attribute.profile.class
#'
#' @param x \code{\link{attribute.profile.class}} input object
#' @method head attribute.profile.class
#' @export
setMethod(
f = "head",
signature = signature(x = 'attribute.profile.class'),
definition = function(x)
{
head(x@results)
}
)
#' summary attribute.profile.class
#'
#' @param object \code{\link{attribute.profile.class}} input object
#' @param verbose a logical. If TRUE, additional diagnostics are printed.
#' @method summary attribute.profile.class
#' @export
setMethod(
f = "summary",
signature = signature(object = 'attribute.profile.class'),
definition = function(object, verbose = TRUE, ...){
results <- slot(object, "results")
nresults <- nrow(results)
pmatrix <- slot(object, "attribute.profile.matrix")
attribute.profile <- ddply(results, .(max.class), summarize, counts = length(max.class),
proportion = length(max.class))
attribute.profile$proportion <- with(attribute.profile, round(proportion/nresults, 3))
attribute.profile <- rename(attribute.profile, c('max.class' = 'attribute.profile'))
attribute.profile <- cbind(attribute.profile, pmatrix[attribute.profile$attribute.profile,])
attribute.profile <- attribute.profile[, c('attribute.profile', colnames(pmatrix), 'counts', 'proportion')]
if (verbose){
cat(sprintf("\nNumber of attribute profiles: %d", nrow(attribute.profile)))
cat("\nAttribute profile counts and proportion: \n")
print(as.data.frame(attribute.profile, row.names = NULL))
}
invisible(attribute.profile)
}
)
#' plot attribute.profile.class
#'
#' @param x \code{\link{attribute.profile.class}} input object
#' @param type a string containing either \code{mean} or \code{profile}
#' @method plot attribute.profile.class
#' @export
setMethod(
f = "plot",
signature = signature(x = 'attribute.profile.class', y = "missing"),
definition = function(x, y, type = 'mean', ...)
{
results <- slot(x, "results")
if (type == 'mean'){
pmatrix <- slot(x, "attribute.profile.matrix")
melted.attr.profile <- melt(results, id.vars = "id", measure.vars = grep('[0-9]+', names(results), )
, value.name = "mean.attr.profile", variable.name = "attr.profile.number")
means.attr.profile <- ddply(melted.attr.profile, .(attr.profile.number), summarize, mean.attr.profile = mean(mean.attr.profile))
means.attr.profile$profile.labels <- sapply(as.numeric(levels(means.attr.profile$attr.profile.number)), function(x) paste(pmatrix[x, ], collapse = ","))
print(ggplot(means.attr.profile, aes(x = attr.profile.number, y = mean.attr.profile, fill = attr.profile.number)) + geom_bar(stat = "identity") +
scale_fill_discrete(name = "Attribute Profile", labels = means.attr.profile$profile.labels) +
ylim(0,1) + ylab("Mean Mastery Proportion") + xlab("Attribute Profile") + ggtitle("Mean Attribute Profile Mastery"))
}
if (type == 'profile'){
ngroups <- ncol(results) - 2
PlotSkillMasteryTableplot(results, ngroups, is.max.class = TRUE)
}
}
)
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