# R/SDistribution_Degenerate.R In distr6: The Complete R6 Probability Distributions Interface

# nolint start
#' @name Degenerate
#' @template SDist
#' @templateVar ClassName Degenerate
#' @templateVar DistName Degenerate
#' @templateVar uses to model deterministic events or as a representation of the delta, or Heaviside, function
#' @templateVar params mean, \eqn{\mu}
#' @templateVar pdfpmf pmf
#' @templateVar pdfpmfeq \deqn{f(x) = 1, \ if \ x = \mu}{f(x) = 1, if x = \mu}\deqn{f(x) = 0, \ if \ x \neq \mu}{f(x) = 0, if x != \mu}
#' @templateVar paramsupport \eqn{\mu \epsilon R}
#' @templateVar distsupport \eqn{{\mu}}
#' @templateVar aka Dirac
#' @templateVar default mean = 0
#'
#' @aliases Dirac Delta
# nolint end
#' @template class_distribution
#' @template method_mode
#' @template method_entropy
#' @template method_kurtosis
#' @template method_pgf
#' @template method_mgfcf
#' @template method_setParameterValue
#' @template param_decorators
#'
#' @family discrete distributions
#' @family univariate distributions
#'
#' @export
Degenerate <- R6Class("Degenerate",
inherit = SDistribution, lock_objects = F,
public = list(
# Public fields
name = "Degenerate",
short_name = "Degen",
description = "Degenerate Probability Distribution.",

# Public methods
# initialize

#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#' @param mean numeric(1) \cr
#' Mean of the distribution, defined on the Reals.
initialize = function(mean = NULL, decorators = NULL) {
super$initialize( decorators = decorators, support = Set$new(0, class = "numeric"),
symmetry = "sym",
type = Reals$new() ) }, # stats #' @description #' The arithmetic mean of a (discrete) probability distribution X is the expectation #' \deqn{E_X(X) = \sum p_X(x)*x} #' with an integration analogue for continuous distributions. #' @param ... Unused. mean = function(...) { unlist(self$getParameterValue("mean"))
},

#' @description
#' The mode of a probability distribution is the point at which the pdf is
#' a local maximum, a distribution can be unimodal (one maximum) or multimodal (several
#' maxima).
mode = function(which = "all") {
unlist(self$getParameterValue("mean")) }, #' @description #' The variance of a distribution is defined by the formula #' \deqn{var_X = E[X^2] - E[X]^2} #' where \eqn{E_X} is the expectation of distribution X. If the distribution is multivariate the #' covariance matrix is returned. #' @param ... Unused. variance = function(...) { numeric(length(self$getParameterValue("mean")))
},

#' @description
#' The skewness of a distribution is defined by the third standardised moment,
#' \deqn{sk_X = E_X[\frac{x - \mu}{\sigma}^3]}{sk_X = E_X[((x - \mu)/\sigma)^3]}
#' where \eqn{E_X} is the expectation of distribution X, \eqn{\mu} is the mean of the
#' distribution and \eqn{\sigma} is the standard deviation of the distribution.
#' @param ... Unused.
skewness = function(...) {
numeric(length(self$getParameterValue("mean"))) }, #' @description #' The kurtosis of a distribution is defined by the fourth standardised moment, #' \deqn{k_X = E_X[\frac{x - \mu}{\sigma}^4]}{k_X = E_X[((x - \mu)/\sigma)^4]} #' where \eqn{E_X} is the expectation of distribution X, \eqn{\mu} is the mean of the #' distribution and \eqn{\sigma} is the standard deviation of the distribution. #' Excess Kurtosis is Kurtosis - 3. #' @param ... Unused. kurtosis = function(excess = TRUE, ...) { rep(NaN, length(self$getParameterValue("mean")))
},

#' @description
#' The entropy of a (discrete) distribution is defined by
#' \deqn{- \sum (f_X)log(f_X)}
#' where \eqn{f_X} is the pdf of distribution X, with an integration analogue for
#' continuous distributions.
#' @param ... Unused.
entropy = function(base = 2, ...) {
numeric(length(self$getParameterValue("mean"))) }, #' @description The moment generating function is defined by #' \deqn{mgf_X(t) = E_X[exp(xt)]} #' where X is the distribution and \eqn{E_X} is the expectation of the distribution X. #' @param ... Unused. mgf = function(t, ...) { return(exp(self$getParameterValue("mean") * t))
},

#' @description The characteristic function is defined by
#' \deqn{cf_X(t) = E_X[exp(xti)]}
#' where X is the distribution and \eqn{E_X} is the expectation of the distribution X.
#' @param ... Unused.
cf = function(t, ...) {
return(exp(self$getParameterValue("mean") * t * 1i)) } ), active = list( #' @field properties #' Returns distribution properties, including skewness type and symmetry. properties = function() { prop <- super$properties
prop$support <- Set$new(self$getParameterValue("mean"), class = "numeric") prop } ), private = list( # dpqr .pdf = function(x, log = FALSE) { mean <- self$getParameterValue("mean")

if (checkmate::testList(mean)) {
return(C_DegeneratePdf(x, unlist(mean), log))
} else {
return(as.numeric(C_DegeneratePdf(x, mean, log)))
}
},
.cdf = function(x, lower.tail = TRUE, log.p = FALSE) {
mean <- self$getParameterValue("mean") if (checkmate::testList(mean)) { return(C_DegenerateCdf(x, unlist(mean), lower.tail, log.p)) } else { return(as.numeric(C_DegenerateCdf(x, mean, lower.tail, log.p))) } }, .quantile = function(p, lower.tail = TRUE, log.p = FALSE) { mean <- self$getParameterValue("mean")

if (checkmate::testList(mean)) {
return(C_DegenerateQuantile(p, unlist(mean), lower.tail, log.p))
} else {
return(as.numeric(C_DegenerateQuantile(p, mean, lower.tail, log.p)))
}
},
.rand = function(n) {
mean <- self$getParameterValue("mean") if (checkmate::testList(mean)) { return(data.table(matrix(rep(mean, n), nrow = n))) } else { return(rep(mean, n)) } }, # traits .traits = list(valueSupport = "discrete", variateForm = "univariate") ) ) .distr6$distributions <- rbind(
.distr6\$distributions,
data.table::data.table(
ShortName = "Degen", ClassName = "Degenerate",
Type = "\u211D", ValueSupport = "discrete",
VariateForm = "univariate",
Package = "-", Tags = "limits"
)
)


## Try the distr6 package in your browser

Any scripts or data that you put into this service are public.

distr6 documentation built on March 28, 2022, 1:05 a.m.