Categorical {distributions3}

Create a Categorical distribution

Description

Create a Categorical distribution

Usage

Categorical(outcomes, p = NULL)

Arguments

outcomes

A vector specifying the elements in the sample space. Can be numeric, factor, character, or logical.

p

A vector of success probabilities for each outcome. Each element of p can be any positive value – the vector gets normalized internally. Defaults to NULL, in which case the distribution is assumed to be uniform.

Value

A Categorical object.

See Also

Other discrete distributions: Bernoulli(), Binomial(), Geometric(), HurdleNegativeBinomial(), HurdlePoisson(), HyperGeometric(), Multinomial(), NegativeBinomial(), Poisson(), ZINegativeBinomial(), ZIPoisson(), ZTNegativeBinomial(), ZTPoisson()

Examples

set.seed(27)

X <- Categorical(1:3, p = c(0.4, 0.1, 0.5))
X

Y <- Categorical(LETTERS[1:4])
Y

random(X, 10)
random(Y, 10)

pdf(X, 1)
log_pdf(X, 1)

cdf(X, 1)
quantile(X, 0.5)

# cdfs are only defined for numeric sample spaces. this errors!
# cdf(Y, "a")

# same for quantiles. this also errors!
# quantile(Y, 0.7)