Categorical | R Documentation |

Mathematical and statistical functions for the Categorical distribution, which is commonly used in classification supervised learning.

The Categorical distribution parameterised with a given support set, *x_1,...,x_k*, and respective probabilities, *p_1,...,p_k*, is defined by the pmf,

*f(x_i) = p_i*

for *p_i, i = 1,…,k; ∑ p_i = 1*.

Sampling from this distribution is performed with the sample function with the elements given
as the support set and the probabilities from the `probs`

parameter. The cdf and quantile assumes
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.

Returns an R6 object inheriting from class SDistribution.

The distribution is supported on *x_1,...,x_k*.

Cat(elements = 1, probs = 1)

N/A

N/A

`distr6::Distribution`

-> `distr6::SDistribution`

-> `Categorical`

`name`

Full name of distribution.

`short_name`

Short name of distribution for printing.

`description`

Brief description of the distribution.

`properties`

Returns distribution properties, including skewness type and symmetry.

`new()`

Creates a new instance of this R6 class.

Categorical$new(elements = NULL, probs = NULL, decorators = NULL)

`elements`

`list()`

Categories in the distribution, see examples.`probs`

`numeric()`

Probabilities of respective categories occurring.`decorators`

`(character())`

Decorators to add to the distribution during construction.

# Note probabilities are automatically normalised (if not vectorised) x <- Categorical$new(elements = list("Bapple", "Banana", 2), probs = c(0.2, 0.4, 1)) # Length of elements and probabilities cannot be changed after construction x$setParameterValue(probs = c(0.1, 0.2, 0.7)) # d/p/q/r x$pdf(c("Bapple", "Carrot", 1, 2)) x$cdf("Banana") # Assumes ordered in construction x$quantile(0.42) # Assumes ordered in construction x$rand(10) # Statistics x$mode() summary(x)

`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.

Categorical$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).

Categorical$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.

Categorical$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.

Categorical$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.

Categorical$kurtosis(excess = TRUE, ...)

`excess`

`(logical(1))`

If`TRUE`

(default) excess kurtosis returned.`...`

Unused.

`entropy()`

The entropy of a (discrete) distribution is defined by

*- ∑ (f_X)log(f_X)*

where *f_X* is the pdf of distribution X, with an integration analogue for
continuous distributions.

Categorical$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.

Categorical$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.

Categorical$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.

Categorical$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.

Categorical$clone(deep = FALSE)

`deep`

Whether 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`

,
`Degenerate`

,
`DiscreteUniform`

,
`EmpiricalMV`

,
`Empirical`

,
`Geometric`

,
`Hypergeometric`

,
`Logarithmic`

,
`Matdist`

,
`Multinomial`

,
`NegativeBinomial`

,
`WeightedDiscrete`

Other univariate distributions:
`Arcsine`

,
`Bernoulli`

,
`BetaNoncentral`

,
`Beta`

,
`Binomial`

,
`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`

## ------------------------------------------------ ## Method `Categorical$new` ## ------------------------------------------------ # Note probabilities are automatically normalised (if not vectorised) x <- Categorical$new(elements = list("Bapple", "Banana", 2), probs = c(0.2, 0.4, 1)) # Length of elements and probabilities cannot be changed after construction x$setParameterValue(probs = c(0.1, 0.2, 0.7)) # d/p/q/r x$pdf(c("Bapple", "Carrot", 1, 2)) x$cdf("Banana") # Assumes ordered in construction x$quantile(0.42) # Assumes ordered in construction x$rand(10) # Statistics x$mode() summary(x)

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