# Categorical: Create a Categorical distribution In distributions3: Probability Distributions as S3 Objects

 Categorical R Documentation

## 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.

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)
```

distributions3 documentation built on Sept. 7, 2022, 5:07 p.m.