DiscretePareto {tolerance} | R Documentation |
Discrete Pareto Distribution
Description
Density (mass), distribution function, quantile function, and random generation for the discrete Pareto distribution.
Usage
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)
Arguments
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 |
Details
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.
Value
ddpareto
gives the density (mass), pdpareto
gives the distribution function, qdpareto
gives the quantile function, and rdpareto
generates random deviates for the specified distribution.
References
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.
See Also
runif
and .Random.seed
about random number generation.
Examples
## 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)