DiscreteLaplace | R Documentation |
Probability mass, distribution function and random generation for the discrete Laplace distribution parametrized by location and scale.
ddlaplace(x, location, scale, log = FALSE)
pdlaplace(q, location, scale, lower.tail = TRUE, log.p = FALSE)
rdlaplace(n, location, scale)
x , q |
vector of quantiles. |
location |
location parameter. |
scale |
scale parameter; |
log , log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are |
n |
number of observations. If |
If U \sim \mathrm{Geometric}(1-p)
and
V \sim \mathrm{Geometric}(1-p)
,
then U-V \sim \mathrm{DiscreteLaplace}(p)
,
where geometric distribution is related to discrete Laplace distribution
in similar way as exponential distribution is related to Laplace distribution.
Probability mass function
f(x) = \frac{1-p}{1+p} p^{|x-\mu|}
Cumulative distribution function
F(x) = \left\{\begin{array}{ll}
\frac{p^{-|x-\mu|}}{1+p} & x < 0 \\
1 - \frac{p^{|x-\mu|+1}}{1+p} & x \ge 0
\end{array}\right.
Inusah, S., & Kozubowski, T.J. (2006). A discrete analogue of the Laplace distribution. Journal of statistical planning and inference, 136(3), 1090-1102.
Kotz, S., Kozubowski, T., & Podgorski, K. (2012). The Laplace distribution and generalizations: a revisit with applications to communications, economics, engineering, and finance. Springer Science & Business Media.
p <- 0.45
x <- rdlaplace(1e5, 0, p)
xx <- seq(-200, 200, by = 1)
plot(prop.table(table(x)))
lines(xx, ddlaplace(xx, 0, p), col = "red")
hist(pdlaplace(x, 0, p))
plot(ecdf(x))
lines(xx, pdlaplace(xx, 0, p), col = "red", type = "s")
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