distr6 (version 1.3.6)

WeightedDiscrete: WeightedDiscrete Distribution Class

Description

Mathematical and statistical functions for the WeightedDiscrete distribution, which is commonly used in empirical estimators such as Kaplan-Meier.

Value

Returns an R6 object inheriting from class SDistribution.

Constructor

WeightedDiscrete$new(data, decorators = NULL, verbose = FALSE)

Constructor Arguments

Argument	Type	Details
data	data.frame	matrix-style object of observations and probabilities. See details.

decorators Decorator decorators to add functionality. See details.

Constructor Details

The WeightedDiscrete distribution is parameterised with an object that can be coerced to a data.frame containing columns 'sample' and at least one of 'pdf' and 'cdf', see examples.

Public Variables

Variable	Return
name	Name of distribution.
short_name	Id of distribution.
description	Brief description of distribution.

Public Methods

Accessor Methods	Link
decorators	decorators
traits	traits
valueSupport	valueSupport
variateForm	variateForm
type	type
properties	properties
support	support
symmetry	symmetry
sup	sup
inf	inf
dmax	dmax
dmin	dmin
skewnessType	skewnessType
kurtosisType	kurtosisType

Statistical Methods Link pdf(x1, ..., log = FALSE, simplify = TRUE) pdf cdf(x1, ..., lower.tail = TRUE, log.p = FALSE, simplify = TRUE) cdf quantile(p, ..., lower.tail = TRUE, log.p = FALSE, simplify = TRUE) quantile.Distribution rand(n, simplify = TRUE) rand mean() mean.Distribution variance() variance stdev() stdev prec() prec cor() cor skewness() skewness kurtosis(excess = TRUE) kurtosis entropy(base = 2) entropy mgf(t) mgf cf(t) cf pgf(z) pgf median() median.Distribution iqr() iqr mode(which = "all") mode

Parameter Methods Link parameters(id) parameters getParameterValue(id, error = "warn") getParameterValue setParameterValue(..., lst = NULL, error = "warn") setParameterValue

Validation Methods Link liesInSupport(x, all = TRUE, bound = FALSE) liesInSupport liesInType(x, all = TRUE, bound = FALSE) liesInType

Representation Methods Link strprint(n = 2) strprint print(n = 2) print summary(full = T) summary.Distribution

Details

The WeightedDiscrete distribution is defined by the pmf, $$f(x_i) = p_i$$ for \(p_i, i = 1,\ldots,k; \sum p_i = 1\).

The distribution is supported on \(x_1,...,x_k\).

Sampling from this distribution is performed with the sample function with the elements given as the x values and the pdf as the probabilities. The cdf and quantile assumes that the elements are supplied in an indexed order (otherwise the results are meaningless).

References

McLaughlin, M. P. (2001). A compendium of common probability distributions (pp. 2014-01). Michael P. McLaughlin.

See Also

listDistributions for all available distributions. sample for the sampling function and Empirical for the closely related Empirical distribution.

Examples

# NOT RUN {
x = WeightedDiscrete$new(data = data.frame(x = 1:3, pdf = c(1/5, 3/5, 1/5)))
WeightedDiscrete$new(data = data.frame(x = 1:3, cdf = c(1/5, 4/5, 1))) # equivalently

# d/p/q/r
x$pdf(1:5)
x$cdf(1:5) # Assumes ordered in construction
x$quantile(0.42) # Assumes ordered in construction
x$rand(10)

# Statistics
x$mean()
x$variance()

summary(x)

# }