
Mathematical and statistical functions for the Logistic distribution, which is commonly used in logistic regression and feedforward neural networks.
Returns an R6 object inheriting from class SDistribution.
The distribution is supported on the Reals.
Logis(mean = 0, scale = 1)
N/A
N/A
distr6::Distribution
-> distr6::SDistribution
-> Logistic
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.
new()
Creates a new instance of this R6 class.
Logistic$new(mean = NULL, scale = NULL, sd = NULL, decorators = NULL)
mean
(numeric(1))
Mean of the distribution, defined on the Reals.
scale
(numeric(1))
Scale parameter, defined on the positive Reals.
sd
(numeric(1))
Standard deviation of the distribution as an alternate scale parameter,
sd = scale*pi/sqrt(3)
. If given then scale
is ignored.
decorators
(character())
Decorators to add to the distribution during construction.
mean()
The arithmetic mean of a (discrete) probability distribution X is the expectation
Logistic$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).
Logistic$mode(which = "all")
which
(character(1) | numeric(1)
Ignored if distribution is unimodal. Otherwise "all"
returns all modes, otherwise specifies
which mode to return.
variance()
The variance of a distribution is defined by the formula
Logistic$variance(...)
...
Unused.
skewness()
The skewness of a distribution is defined by the third standardised moment,
Logistic$skewness(...)
...
Unused.
kurtosis()
The kurtosis of a distribution is defined by the fourth standardised moment,
Logistic$kurtosis(excess = TRUE, ...)
excess
(logical(1))
If TRUE
(default) excess kurtosis returned.
...
Unused.
entropy()
The entropy of a (discrete) distribution is defined by
Logistic$entropy(base = 2, ...)
base
(integer(1))
Base of the entropy logarithm, default = 2 (Shannon entropy)
...
Unused.
mgf()
The moment generating function is defined by
Logistic$mgf(t, ...)
t
(integer(1))
t integer to evaluate function at.
...
Unused.
cf()
The characteristic function is defined by
Logistic$cf(t, ...)
t
(integer(1))
t integer to evaluate function at.
...
Unused.
pgf()
The probability generating function is defined by
Logistic$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.
Logistic$clone(deep = FALSE)
deep
Whether to make a deep clone.
The Logistic distribution parameterised with mean,
McLaughlin, M. P. (2001). A compendium of common probability distributions (pp. 2014-01). Michael P. McLaughlin.
Other continuous distributions:
Arcsine
,
BetaNoncentral
,
Beta
,
Cauchy
,
ChiSquaredNoncentral
,
ChiSquared
,
Dirichlet
,
Erlang
,
Exponential
,
FDistributionNoncentral
,
FDistribution
,
Frechet
,
Gamma
,
Gompertz
,
Gumbel
,
InverseGamma
,
Laplace
,
Loglogistic
,
Lognormal
,
MultivariateNormal
,
Normal
,
Pareto
,
Poisson
,
Rayleigh
,
ShiftedLoglogistic
,
StudentTNoncentral
,
StudentT
,
Triangular
,
Uniform
,
Wald
,
Weibull
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
,
Loglogistic
,
Lognormal
,
NegativeBinomial
,
Normal
,
Pareto
,
Poisson
,
Rayleigh
,
ShiftedLoglogistic
,
StudentTNoncentral
,
StudentT
,
Triangular
,
Uniform
,
Wald
,
Weibull
,
WeightedDiscrete