| DiscretePareto | R Documentation |
Density (mass), distribution function, quantile function, and random generation for the discrete Pareto distribution.
ddpareto(x, theta, log = FALSE)
pdpareto(q, theta, lower.tail = TRUE, log.p = FALSE)
qdpareto(p, theta, lower.tail = TRUE, log.p = FALSE)
rdpareto(n, theta)
x, q |
Vector of quantiles. |
p |
Vector of probabilities. |
n |
The number of observations. If |
theta |
The shape parameter, which must be greater than 0 and less than 1. |
log, log.p |
Logical vectors. If |
lower.tail |
Logical vector. If |
The discrete Pareto distribution has mass
p(x) = \theta^{\log(1+x)}-\theta^{\log(2+x)},
where x=0,1,\ldots and 0<\theta<1 is the shape parameter.
ddpareto gives the density (mass), pdpareto gives the distribution function, qdpareto gives the quantile function, and rdpareto generates random deviates for the specified distribution.
Krishna, H. and Pundir, P. S. (2009), Discrete Burr and Discrete Pareto Distributions, Statistical Methodology, 6, 177–188.
Young, D. S., Naghizadeh Qomi, M., and Kiapour, A. (2019), Approximate Discrete Pareto Tolerance Limits for Characterizing Extremes in Count Data, Statistica Neerlandica, 73, 4–21.
runif and .Random.seed about random number generation.
## Randomly generated data from the discrete Pareto
## distribution.
set.seed(100)
x <- rdpareto(n = 150, theta = 0.2)
hist(x, main = "Randomly Generated Data", prob = TRUE)
x.1 <- sort(x)
y <- ddpareto(x = x.1, theta = 0.2)
lines(x.1, y, col = 2, lwd = 2)
plot(x.1, pdpareto(q = x.1, theta = 0.2), type = "l",
xlab = "x", ylab = "Cumulative Probabilities")
qdpareto(p = 0.80, theta = 0.2, lower.tail = FALSE)
qdpareto(p = 0.95, theta = 0.2)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.