Noncentral Chi-Squared Distribution Class
Mathematical and statistical functions for the Noncentral Chi-Squared distribution, which is commonly used to model the sum of independent squared Normal distributions and for confidence intervals.
The Noncentral Chi-Squared distribution parameterised with degrees of freedom, ν, and location, λ, is defined by the pdf,
f(x) = exp(-λ/2) ∑_{r=0}^∞ ((λ/2)^r/r!) (x^{(ν+2r)/2-1}exp(-x/2))/(2^{(ν+2r)/2}Γ((ν+2r)/2))
for ν ≥ 0, λ ≥ 0.
Returns an R6 object inheriting from class SDistribution.
The distribution is supported on the Positive Reals.
ChiSqNC(df = 1, location = 0)
N/A
N/A
distr6::Distribution -> distr6::SDistribution -> ChiSquaredNoncentral
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.
ChiSquaredNoncentral$new(df = NULL, location = NULL, decorators = NULL)
df(integer(1))
Degrees of freedom of the distribution defined on the positive Reals.
location(numeric(1))
Location parameter, defined on the non-negative Reals.
decorators(character())
Decorators to add to the distribution during construction.
mean()
The arithmetic mean of a (discrete) probability distribution X is the expectation
E_X(X) = ∑ p_X(x)*x
with an integration analogue for continuous distributions.
ChiSquaredNoncentral$mean(...)
...Unused.
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.
ChiSquaredNoncentral$variance(...)
...Unused.
skewness()
The skewness of a distribution is defined by the third standardised moment,
sk_X = E_X[((x - μ)/σ)^3]
where E_X is the expectation of distribution X, μ is the mean of the distribution and σ is the standard deviation of the distribution.
ChiSquaredNoncentral$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.
ChiSquaredNoncentral$kurtosis(excess = TRUE, ...)
excess(logical(1))
If TRUE (default) excess kurtosis returned.
...Unused.
mgf()
The moment generating function is defined by
mgf_X(t) = E_X[exp(xt)]
where X is the distribution and E_X is the expectation of the distribution X.
ChiSquaredNoncentral$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.
ChiSquaredNoncentral$cf(t, ...)
t(integer(1))
t integer to evaluate function at.
...Unused.
setParameterValue()
Sets the value(s) of the given parameter(s).
ChiSquaredNoncentral$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.
ChiSquaredNoncentral$clone(deep = FALSE)
deepWhether to make a deep clone.
Jordan Deenichin
McLaughlin, M. P. (2001). A compendium of common probability distributions (pp. 2014-01). Michael P. McLaughlin.
Other continuous distributions:
Arcsine,
BetaNoncentral,
Beta,
Cauchy,
ChiSquared,
Dirichlet,
Erlang,
Exponential,
FDistributionNoncentral,
FDistribution,
Frechet,
Gamma,
Gompertz,
Gumbel,
InverseGamma,
Laplace,
Logistic,
Loglogistic,
Lognormal,
MultivariateNormal,
Normal,
Pareto,
Poisson,
Rayleigh,
ShiftedLoglogistic,
StudentTNoncentral,
StudentT,
Triangular,
Uniform,
Wald,
Weibull
Other univariate distributions:
Arcsine,
Bernoulli,
BetaNoncentral,
Beta,
Binomial,
Categorical,
Cauchy,
ChiSquared,
Degenerate,
DiscreteUniform,
Empirical,
Erlang,
Exponential,
FDistributionNoncentral,
FDistribution,
Frechet,
Gamma,
Geometric,
Gompertz,
Gumbel,
Hypergeometric,
InverseGamma,
Laplace,
Logarithmic,
Logistic,
Loglogistic,
Lognormal,
NegativeBinomial,
Normal,
Pareto,
Poisson,
Rayleigh,
ShiftedLoglogistic,
StudentTNoncentral,
StudentT,
Triangular,
Uniform,
Wald,
Weibull,
WeightedDiscrete
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