Negative Binomial Distribution Class
Mathematical and statistical functions for the Negative Binomial distribution, which is commonly used to model the number of successes, trials or failures before a given number of failures or successes.
The Negative Binomial distribution parameterised with number of failures before successes, n, and probability of success, p, is defined by the pmf,
f(x) = C(x + n - 1, n - 1) p^n (1 - p)^x
for n = {0,1,2,…} and probability p, where C(a,b) is the combination (or binomial coefficient) function.
The Negative Binomial distribution can refer to one of four distributions (forms):
The number of failures before K successes (fbs)
The number of successes before K failures (sbf)
The number of trials before K failures (tbf)
The number of trials before K successes (tbs)
For each we refer to the number of K successes/failures as the size parameter.
Note that the size parameter is not currently vectorised in VectorDistributions.
Returns an R6 object inheriting from class SDistribution.
The distribution is supported on {0,1,2,…} (for fbs and sbf) or {n,n+1,n+2,…} (for tbf and tbs) (see below).
NBinom(size = 10, prob = 0.5, form = "fbs")
N/A
N/A
distr6::Distribution -> distr6::SDistribution -> NegativeBinomial
nameFull name of distribution.
short_nameShort name of distribution for printing.
descriptionBrief description of the distribution.
packagesPackages required to be installed in order to construct the distribution.
new()
Creates a new instance of this R6 class.
NegativeBinomial$new( size = NULL, prob = NULL, qprob = NULL, mean = NULL, form = NULL, decorators = NULL )
size(integer(1))
Number of trials/successes.
prob(numeric(1))
Probability of success.
qprob(numeric(1))
Probability of failure. If provided then prob is ignored. qprob = 1 - prob.
mean(numeric(1))
Mean of distribution, alternative to prob and qprob.
formcharacter(1))
Form of the distribution, cannot be changed after construction. Options are to model
the number of,
"fbs" - Failures before successes.
"sbf" - Successes before failures.
"tbf" - Trials before failures.
"tbs" - Trials before successes.
Use $description to see the Negative Binomial form.
decorators(character())
Decorators to add to the distribution during construction.
mean()
The arithmetic mean of a (discrete) probability distribution X is the expectation
E_X(X) = ∑ p_X(x)*x
with an integration analogue for continuous distributions.
NegativeBinomial$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).
NegativeBinomial$mode(which = "all")
which(character(1) | numeric(1)
Ignored if distribution is unimodal. Otherwise "all" returns all modes, otherwise specifies
which mode to return.
variance()
The variance of a distribution is defined by the formula
var_X = E[X^2] - E[X]^2
where E_X is the expectation of distribution X. If the distribution is multivariate the covariance matrix is returned.
NegativeBinomial$variance(...)
...Unused.
skewness()
The skewness of a distribution is defined by the third standardised moment,
sk_X = E_X[((x - μ)/σ)^3]
where E_X is the expectation of distribution X, μ is the mean of the distribution and σ is the standard deviation of the distribution.
NegativeBinomial$skewness(...)
...Unused.
kurtosis()
The kurtosis of a distribution is defined by the fourth standardised moment,
k_X = E_X[((x - μ)/σ)^4]
where E_X is the expectation of distribution X, μ is the mean of the distribution and σ is the standard deviation of the distribution. Excess Kurtosis is Kurtosis - 3.
NegativeBinomial$kurtosis(excess = TRUE, ...)
excess(logical(1))
If TRUE (default) excess kurtosis returned.
...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.
NegativeBinomial$mgf(t, ...)
t(integer(1))
t integer to evaluate function at.
...Unused.
cf()
The characteristic function is defined by
cf_X(t) = E_X[exp(xti)]
where X is the distribution and E_X is the expectation of the distribution X.
NegativeBinomial$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.
NegativeBinomial$pgf(z, ...)
z(integer(1))
z integer to evaluate probability generating function at.
...Unused.
setParameterValue()
Sets the value(s) of the given parameter(s).
NegativeBinomial$setParameterValue( ..., lst = NULL, error = "warn", resolveConflicts = FALSE )
...ANY
Named arguments of parameters to set values for. See examples.
lst(list(1))
Alternative argument for passing parameters. List names should be parameter names and list values
are the new values to set.
error(character(1))
If "warn" then returns a warning on error, otherwise breaks if "stop".
resolveConflicts(logical(1))
If FALSE (default) throws error if conflicting parameterisations are provided, otherwise
automatically resolves them by removing all conflicting parameters.
clone()
The objects of this class are cloneable with this method.
NegativeBinomial$clone(deep = FALSE)
deepWhether to make a deep clone.
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,
Multinomial,
WeightedDiscrete
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,
Normal,
Pareto,
Poisson,
Rayleigh,
ShiftedLoglogistic,
StudentTNoncentral,
StudentT,
Triangular,
Uniform,
Wald,
Weibull,
WeightedDiscrete
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