Create a Categorical distribution

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

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
# \donttest{ # cdfs are only defined for numeric sample spaces. this errors! cdf(Y, "a")
#> Error: The sample space of `x` must be numeric to evaluate the cdf.
# same for quantiles. this also errors! quantile(Y, 0.7)
#> Error: The sample space of `x` must be numeric to evaluate quantiles.
# }