#' Binary class: Binary predictions
#'
#' A \code{predx} class for binary probabilistic predictions.
#'
#' A single numeric probability that is greater than or equal to 0 and less than or equal to 1.
#'
#' In JSON and CSV representations, this probability is named \code{prob}.
#'
#' @slot predx A single numeric probability.
#'
#' @export
#' @include transform_predx.R predx_to_json.R
#'
#' @examples
setClass('Binary', #S4 class
slots = c(predx = 'numeric')
)
setValidity('Binary', function(object) {
### structure checks
collect_tests <- check_no_NAs(object@predx)
### content checks
if (all(collect_tests == TRUE)) {
collect_tests <- c(collect_tests,
check_probs_gt0(object@predx),
check_probs_lt1(object@predx),
check_single_value(object@predx)
)
}
if (all(collect_tests == TRUE)) TRUE
else collect_tests[collect_tests != TRUE]
})
#' @export
#' @rdname Binary-class
Binary <- function(x) {
if (is.list(x)) x <- x$prob
new("Binary", predx = x)
}
lapply_Binary <- function(x) {
lapply(x, function(x, ...) tryCatch(Binary(x),
error=function(e) identity(e)))
}
#' @export
#' @rdname Binary-class
is.Binary <- function(object) {
class(object) == 'Binary'
}
#' @export
#' @rdname Binary-class
setMethod("predx_to_json", "Binary",
function(x) { list(prob = x@predx) })
#' @export
#' @rdname Binary-class
setMethod("as.data.frame", "Binary",
function(x, ...) { data.frame(prob = x@predx) })
#' @export
#' @rdname Binary-class
setMethod("transform_predx", "Binary",
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)
}
})
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