Mathematical and statistical functions for the Binomial distribution, which is commonly used to model the number of successes out of a number of independent trials.
Returns an R6 object inheriting from class SDistribution.
The distribution is supported on
Binom(size = 10, prob = 0.5)
N/A
N/A
distr6::Distribution
-> distr6::SDistribution
-> Binomial
name
Full name of distribution.
short_name
Short name of distribution for printing.
description
Brief description of the distribution.
packages
Packages required to be installed in order to construct the distribution.
properties
Returns distribution properties, including skewness type and symmetry.
new()
Creates a new instance of this R6 class.
Binomial$new(size = NULL, prob = NULL, qprob = NULL, decorators = NULL)
size
(integer(1))
Number of trials, defined on the positive Naturals.
prob
(numeric(1))
Probability of success.
qprob
(numeric(1))
Probability of failure. If provided then prob
is ignored. qprob = 1 - prob
.
decorators
(character())
Decorators to add to the distribution during construction.
mean()
The arithmetic mean of a (discrete) probability distribution X is the expectation
Binomial$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).
Binomial$mode(which = "all")
which
(character(1) | numeric(1)
Ignored if distribution is unimodal. Otherwise "all"
returns all modes, otherwise specifies
which mode to return.
variance()
The variance of a distribution is defined by the formula
Binomial$variance(...)
...
Unused.
skewness()
The skewness of a distribution is defined by the third standardised moment,
Binomial$skewness(...)
...
Unused.
kurtosis()
The kurtosis of a distribution is defined by the fourth standardised moment,
Binomial$kurtosis(excess = TRUE, ...)
excess
(logical(1))
If TRUE
(default) excess kurtosis returned.
...
Unused.
entropy()
The entropy of a (discrete) distribution is defined by
Binomial$entropy(base = 2, ...)
base
(integer(1))
Base of the entropy logarithm, default = 2 (Shannon entropy)
...
Unused.
mgf()
The moment generating function is defined by
Binomial$mgf(t, ...)
t
(integer(1))
t integer to evaluate function at.
...
Unused.
cf()
The characteristic function is defined by
Binomial$cf(t, ...)
t
(integer(1))
t integer to evaluate function at.
...
Unused.
pgf()
The probability generating function is defined by
Binomial$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.
Binomial$clone(deep = FALSE)
deep
Whether to make a deep clone.
The Binomial distribution parameterised with number of trials, n, and probability of success, p, is defined by the pmf,
McLaughlin, M. P. (2001). A compendium of common probability distributions (pp. 2014-01). Michael P. McLaughlin.
Other discrete distributions:
Bernoulli
,
Categorical
,
Degenerate
,
DiscreteUniform
,
EmpiricalMV
,
Empirical
,
Geometric
,
Hypergeometric
,
Logarithmic
,
Matdist
,
Multinomial
,
NegativeBinomial
,
WeightedDiscrete
Other univariate distributions:
Arcsine
,
Bernoulli
,
BetaNoncentral
,
Beta
,
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
,
WeightedDiscrete