Categorical {extraDistr}

Categorical distribution

Description

Probability mass function, distribution function, quantile function and random generation for the categorical distribution.

Usage

dcat(x, prob, log = FALSE)

pcat(q, prob, lower.tail = TRUE, log.p = FALSE)

qcat(p, prob, lower.tail = TRUE, log.p = FALSE, labels)

rcat(n, prob, labels)

rcatlp(n, log_prob, labels)

Arguments

x, q

vector of quantiles.

prob, log_prob

vector of length m, or m-column matrix of non-negative weights (or their logarithms in log_prob).

log, log.p

logical; if TRUE, probabilities p are given as log(p).

lower.tail

logical; if TRUE (default), probabilities are P[X \le x] otherwise, P[X > x].

p

vector of probabilities.

labels

if provided, labeled factor vector is returned. Number of labels needs to be the same as number of categories (number of columns in prob).

n

number of observations. If length(n) > 1, the length is taken to be the number required.

Details

Probability mass function

\Pr(X = k) = \frac{w_k}{\sum_{j=1}^m w_j}

Cumulative distribution function

\Pr(X \le k) = \frac{\sum_{i=1}^k w_i}{\sum_{j=1}^m w_j}

It is possible to sample from categorical distribution parametrized by vector of unnormalized log-probabilities \alpha_1,\dots,\alpha_m without leaving the log space by employing the Gumbel-max trick (Maddison, Tarlow and Minka, 2014). If g_1,\dots,g_m are samples from Gumbel distribution with cumulative distribution function F(g) = \exp(-\exp(-g)), then k = \mathrm{arg\,max}_i \{g_i + \alpha_i\} is a draw from categorical distribution parametrized by vector of probabilities p_1,\dots,p_m, such that p_i = \exp(\alpha_i) / [\sum_{j=1}^m \exp(\alpha_j)]. This is implemented in rcatlp function parametrized by vector of log-probabilities log_prob.

References

Maddison, C. J., Tarlow, D., & Minka, T. (2014). A* sampling. [In:] Advances in Neural Information Processing Systems (pp. 3086-3094). https://arxiv.org/abs/1411.0030

Examples

# Generating 10 random draws from categorical distribution
# with k=3 categories occuring with equal probabilities
# parametrized using a vector

rcat(10, c(1/3, 1/3, 1/3))

# or with k=5 categories parametrized using a matrix of probabilities
# (generated from Dirichlet distribution)

p <- rdirichlet(10, c(1, 1, 1, 1, 1))
rcat(10, p)

x <- rcat(1e5, c(0.2, 0.4, 0.3, 0.1))
plot(prop.table(table(x)), type = "h")
lines(0:5, dcat(0:5, c(0.2, 0.4, 0.3, 0.1)), col = "red")

p <- rdirichlet(1, rep(1, 20))
x <- rcat(1e5, matrix(rep(p, 2), nrow = 2, byrow = TRUE))
xx <- 0:21
plot(prop.table(table(x)))
lines(xx, dcat(xx, p), col = "red")

xx <- seq(0, 21, by = 0.01)
plot(ecdf(x))
lines(xx, pcat(xx, p), col = "red", lwd = 2)

pp <- seq(0, 1, by = 0.001)
plot(ecdf(x))
lines(qcat(pp, p), pp, col = "red", lwd = 2)