Create a Categorical distribution

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(), PoissonBinomial(), ZINegativeBinomial(), ZIPoisson(), ZTNegativeBinomial(), ZTPoisson()

Examples

set.seed(27)

X <- Categorical(1:3, p = c(0.4, 0.1, 0.5))
X
#> Categorical distribution
#>   outcomes = [1, 2, 3]
#>   p = [0.4, 0.1, 0.5] 

Y <- Categorical(LETTERS[1:4])
Y
#> Categorical distribution
#>   outcomes = [A, B, ..., D]
#>   p = [0.25, 0.25, ..., 0.25] 

random(X, 10)
#>  [1] 2 3 1 3 3 3 3 3 3 3
random(Y, 10)
#>  [1] "D" "A" "D" "D" "A" "A" "A" "B" "D" "B"

pdf(X, 1)
#> [1] 0.4
log_pdf(X, 1)
#> [1] -0.9162907

cdf(X, 1)
#> [1] 0.4
quantile(X, 0.5)
#> [1] 2

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

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