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Evaluate the probability mass function of a Categorical discrete distribution

Usage

# S3 method for Categorical
pdf(d, x, ...)

# S3 method for Categorical
log_pdf(d, x, ...)

Arguments

d

A Categorical object created by a call to Categorical().

x

A vector of elements whose probabilities you would like to determine given the distribution d.

...

Unused. Unevaluated arguments will generate a warning to catch mispellings or other possible errors.

Value

A vector of probabilities, one for each element of x.

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)