#' PointCat class: Point predictions for categorical variables
#'
#' A \code{predx} class for point predictions for categorical variables.
#'
#' \code{PointCat} objects contain a single point prediction of type character
#' with no other restrictions.
#'
#' In JSON and CSV representations, this value is named \code{point}.
#'
#' @slot predx A single point prediction of type character.
#'
#' @return
#' @export
#' @include transform_predx.R predx_to_json.R
#'
#' @examples
#'
setClass('PointCat', #S4 class
slots = c(predx = 'character')
)
setValidity('PointCat', function(object) {
### structure checks
collect_tests <- c(
check_no_NAs(object@predx),
if (is.character(object@predx)) TRUE else "requires a character value"
)
### content checks
if (all(collect_tests == TRUE)) {
collect_tests <- c(collect_tests,
check_single_value(object@predx)
)
}
if (all(collect_tests == TRUE)) TRUE
else collect_tests[collect_tests != TRUE]
})
#' @export
#' @rdname PointCat-class
PointCat <- function(x) {
new("PointCat", predx = x)
}
lapply_PointCat <- function(x) {
lapply(x, function(x, ...) tryCatch(PointCat(x$point),
error=function(e) identity(e)))
}
#' @export
#' @rdname PointCat-class
is.PointCat <- function(object) {
class(object) == 'PointCat'
}
#' @export
#' @rdname Point-class
setMethod("predx_to_json", "PointCat",
function(x) { list(point = x@predx) })
#' @export
#' @rdname PointCat-class
setMethod("as.data.frame", "PointCat",
function(x, ...) { data.frame(point = x@predx, stringsAsFactors = FALSE) })
#' @export
#' @rdname PointCat-class
setMethod("transform_predx", "PointCat",
function(x, to_class, ...) {
if (to_class == class(x)) {
return(x)
} else {
warning(paste0('NAs introduced by coercion, ', class(x), ' to ',
to_class, ' not available'))
return(NA)
}
})
# methods
setMethod("quantile", "PointCat", function(x) { NA })
setMethod("median", "PointCat", function(x) { NA })
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