Title: | Double Log Normal Distribution Functions |
---|---|
Description: | Functions to sample from the double log normal distribution and calculate the density, distribution and quantile functions. |
Authors: | Olaf Mersmann [aut, cre], Heike Trautmann [ctb], Detlef Steuer [ctb] |
Maintainer: | Olaf Mersmann <[email protected]> |
License: | GPL-2 |
Version: | 1.0.2 |
Built: | 2025-02-19 04:43:29 UTC |
Source: | https://github.com/olafmersmann/loglognorm |
Density, distribution function, quantile function, random generation and expected value function for the double log normal distribution with mean equal to 'mean' and standard deviation equal to 'sd'.
dloglognorm(x, mean = 0, sd = 1) ploglognorm(q, mean = 0, sd = 1) qloglognorm(p, mean = 0, sd = 1) rloglognorm(n, mean = 0, sd = 1) mloglognorm(moment, mean, sd) eloglognorm(mean, sd) vloglognorm(mean, sd)
dloglognorm(x, mean = 0, sd = 1) ploglognorm(q, mean = 0, sd = 1) qloglognorm(p, mean = 0, sd = 1) rloglognorm(n, mean = 0, sd = 1) mloglognorm(moment, mean, sd) eloglognorm(mean, sd) vloglognorm(mean, sd)
x , q
|
vector of quantiles. |
p |
vector of probabilites. |
n |
number of observations. |
mean |
vector of means. |
sd |
vector of standard deviations. |
moment |
vector of moments |
If 'mean' or 'sd' are not specified they assume the default values of '0' and '1', respectively.
'dloglognorm' gives the density, 'ploglognorm' gives the distribution function, 'qloglognorm' gives the quantile function, 'rloglognorm' generates random deviates, 'mloglognorm' returns the rth moment, 'eloglognorm' gives the expected value of the distirbution and vloglognorm the variance.
Heike Trautmann [email protected], Detlef Steuer [email protected] and Olaf Mersmann [email protected]
B. Holland, M. Ahsanullah (1989): Further Resultson the Distribution of Meinhold and Singpurwalla, The American Statistician 43 (4), p. 216-219
x <- seq(0, 1, by=0.05) ## Several different shapes of the density: par(mfrow=c(3, 1)) curve(dloglognorm(x, -0.2, 0.2), 0, 1, main="DLN(-0.2, 0.2)") curve(dloglognorm(x, 0.2, 1.0), 0, 1, main="DLN(0.2, 2.0)") curve(dloglognorm(x, 0.2, 1.8), 0, 1, main="DLN(0.2, 2.0)") ## Check precision: z <- x - pnorm(qnorm(x, .2, 1.0), .2, 1.0) max(z)
x <- seq(0, 1, by=0.05) ## Several different shapes of the density: par(mfrow=c(3, 1)) curve(dloglognorm(x, -0.2, 0.2), 0, 1, main="DLN(-0.2, 0.2)") curve(dloglognorm(x, 0.2, 1.0), 0, 1, main="DLN(0.2, 2.0)") curve(dloglognorm(x, 0.2, 1.8), 0, 1, main="DLN(0.2, 2.0)") ## Check precision: z <- x - pnorm(qnorm(x, .2, 1.0), .2, 1.0) max(z)