dist.Categorical | R Documentation |
This is the density and random deviates function for the categorical
distribution with probabilities parameter p
.
dcat(x, p, log=FALSE)
qcat(pr, p, lower.tail=TRUE, log.pr=FALSE)
rcat(n, p)
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 \sim \mathcal{CAT}(p)
Notation 2: p(\theta) = \mathcal{CAT}(\theta | p)
Parameter 1: probabilities p
Mean: E(\theta)
= Unknown
Variance: var(\theta)
= Unknown
Mode: 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. software@bayesian-inference.com
as.indicator.matrix
,
ddirichlet
, and
dmultinom
.
library(LaplacesDemon)
dcat(x=1, p=c(0.3,0.3,0.4))
rcat(n=10, p=c(0.1,0.3,0.6))
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