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)
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