Mathematical and statistical functions for the WeightedDiscrete distribution, which is commonly used in empirical estimators such as Kaplan-Meier.
Returns an R6 object inheriting from class SDistribution.
The distribution is supported on \(x_1,...,x_k\).
WeightDisc(x = 1, pdf = 1)
N/A
N/A
distr6::Distribution -> distr6::SDistribution -> WeightedDiscrete
nameFull name of distribution.
short_nameShort name of distribution for printing.
descriptionBrief description of the distribution.
propertiesReturns distribution properties, including skewness type and symmetry.
new()Creates a new instance of this R6 class.
WeightedDiscrete$new(x = NULL, pdf = NULL, cdf = NULL, decorators = NULL)
xnumeric()
Data samples, must be ordered in ascending order.
pdfnumeric()
Probability mass function for corresponding samples, should be same length x.
If cdf is not given then calculated as cumsum(pdf).
cdfnumeric()
Cumulative distribution function for corresponding samples, should be same length x. If
given then pdf is ignored and calculated as difference of cdfs.
decorators(character())
Decorators to add to the distribution during construction.
strprint()Printable string representation of the Distribution. Primarily used internally.
WeightedDiscrete$strprint(n = 2)
n(integer(1))
Ignored.
mean()The arithmetic mean of a (discrete) probability distribution X is the expectation $$E_X(X) = \sum p_X(x)*x$$ with an integration analogue for continuous distributions. If distribution is improper (F(Inf) != 1, then E_X(x) = Inf).
WeightedDiscrete$mean(...)
...Unused.
mode()The mode of a probability distribution is the point at which the pdf is a local maximum, a distribution can be unimodal (one maximum) or multimodal (several maxima).
WeightedDiscrete$mode(which = "all")
which(character(1) | numeric(1)
Ignored if distribution is unimodal. Otherwise "all" returns all modes, otherwise specifies
which mode to return.
variance()The variance of a distribution is defined by the formula $$var_X = E[X^2] - E[X]^2$$ where \(E_X\) is the expectation of distribution X. If the distribution is multivariate the covariance matrix is returned. If distribution is improper (F(Inf) != 1, then var_X(x) = Inf).
WeightedDiscrete$variance(...)
...Unused.
skewness()The skewness of a distribution is defined by the third standardised moment, $$sk_X = E_X[\frac{x - \mu}{\sigma}^3]$$ where \(E_X\) is the expectation of distribution X, \(\mu\) is the mean of the distribution and \(\sigma\) is the standard deviation of the distribution. If distribution is improper (F(Inf) != 1, then sk_X(x) = Inf).
WeightedDiscrete$skewness(...)
...Unused.
kurtosis()The kurtosis of a distribution is defined by the fourth standardised moment, $$k_X = E_X[\frac{x - \mu}{\sigma}^4]$$ where \(E_X\) is the expectation of distribution X, \(\mu\) is the mean of the distribution and \(\sigma\) is the standard deviation of the distribution. Excess Kurtosis is Kurtosis - 3. If distribution is improper (F(Inf) != 1, then k_X(x) = Inf).
WeightedDiscrete$kurtosis(excess = TRUE, ...)
excess(logical(1))
If TRUE (default) excess kurtosis returned.
...Unused.
entropy()The entropy of a (discrete) distribution is defined by $$- \sum (f_X)log(f_X)$$ where \(f_X\) is the pdf of distribution X, with an integration analogue for continuous distributions. If distribution is improper then entropy is Inf.
WeightedDiscrete$entropy(base = 2, ...)
base(integer(1))
Base of the entropy logarithm, default = 2 (Shannon entropy)
...Unused.
mgf()The moment generating function is defined by $$mgf_X(t) = E_X[exp(xt)]$$ where X is the distribution and \(E_X\) is the expectation of the distribution X. If distribution is improper (F(Inf) != 1, then mgf_X(x) = Inf).
WeightedDiscrete$mgf(t, ...)
t(integer(1))
t integer to evaluate function at.
...Unused.
cf()The characteristic function is defined by $$cf_X(t) = E_X[exp(xti)]$$ where X is the distribution and \(E_X\) is the expectation of the distribution X. If distribution is improper (F(Inf) != 1, then cf_X(x) = Inf).
WeightedDiscrete$cf(t, ...)
t(integer(1))
t integer to evaluate function at.
...Unused.
pgf()The probability generating function is defined by $$pgf_X(z) = E_X[exp(z^x)]$$ where X is the distribution and \(E_X\) is the expectation of the distribution X. If distribution is improper (F(Inf) != 1, then pgf_X(x) = Inf).
WeightedDiscrete$pgf(z, ...)
z(integer(1))
z integer to evaluate probability generating function at.
...Unused.
clone()The objects of this class are cloneable with this method.
WeightedDiscrete$clone(deep = FALSE)
deepWhether to make a deep clone.
The WeightedDiscrete distribution is defined by the pmf, $$f(x_i) = p_i$$ for \(p_i, i = 1,\ldots,k; \sum p_i = 1\).
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 assume that the elements are supplied in an indexed order (otherwise the results are meaningless).
The number of points in the distribution cannot be changed after construction.
McLaughlin, M. P. (2001). A compendium of common probability distributions (pp. 2014-01). Michael P. McLaughlin.
Other discrete distributions:
Bernoulli,
Binomial,
Categorical,
Degenerate,
DiscreteUniform,
EmpiricalMV,
Empirical,
Geometric,
Hypergeometric,
Logarithmic,
Matdist,
Multinomial,
NegativeBinomial
Other univariate distributions:
Arcsine,
Bernoulli,
BetaNoncentral,
Beta,
Binomial,
Categorical,
Cauchy,
ChiSquaredNoncentral,
ChiSquared,
Degenerate,
DiscreteUniform,
Empirical,
Erlang,
Exponential,
FDistributionNoncentral,
FDistribution,
Frechet,
Gamma,
Geometric,
Gompertz,
Gumbel,
Hypergeometric,
InverseGamma,
Laplace,
Logarithmic,
Logistic,
Loglogistic,
Lognormal,
Matdist,
NegativeBinomial,
Normal,
Pareto,
Poisson,
Rayleigh,
ShiftedLoglogistic,
StudentTNoncentral,
StudentT,
Triangular,
Uniform,
Wald,
Weibull
# NOT RUN {
x <- WeightedDiscrete$new(x = 1:3, pdf = c(1 / 5, 3 / 5, 1 / 5))
WeightedDiscrete$new(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)
# }
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