Description Usage Arguments Details Value Author(s) See Also Examples

This is the density and random deviates function for the categorical
distribution with probabilities parameter *p*.

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`x` |
This is a vector of discrete data with |

`n` |
This is the number of observations, which must be a positive
integer that has length 1. When |

`p` |
This is a vector of length |

`pr` |
This is a vector of probabilities, or log-probabilities. |

`log` |
Logical. If |

`log.pr` |
Logical. if |

`lower.tail` |
Logical. if |

Application: Discrete Univariate

Density:

*p(theta) = Sum (theta * p)*Inventor: Unknown (to me, anyway)

Notation 1:

*theta ~ CAT(p)*Notation 2:

*p(theta) = CAT(theta | p)*Parameter 1: probabilities

*p*Mean:

*E(theta)*= UnknownVariance:

*var(theta)*= UnknownMode:

*mode(theta)*= Unknown

Also called the discrete distribution, the categorical distribution
describes the result of a random event that can take on one of *k*
possible outcomes, with the probability *p* of each outcome
separately specified. The vector *p* of probabilities for each
event must sum to 1. The categorical distribution is often used, for
example, in the multinomial logit model. The conjugate prior is the
Dirichlet distribution.

`dcat`

gives the density and
`rcat`

generates random deviates.

Statisticat, LLC. [email protected]

`as.indicator.matrix`

,
`ddirichlet`

, and
`dmultinom`

.

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