| Version: | 1.0.2 |
| Title: | Double Log Normal Distribution Functions |
| Description: | Functions to sample from the double log normal distribution and calculate the density, distribution and quantile functions. |
| Maintainer: | Olaf Mersmann <olafm@p-value.net> |
| Depends: | R (≥ 4.1.0) |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| Packaged: | 2022-06-11 22:59:24 UTC; olafm |
| Author: | Olaf Mersmann [aut, cre], Heike Trautmann [ctb], Detlef Steuer [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2022-06-12 16:00:02 UTC |
The Double Log Normal Distribution
Description
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'.
Usage
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)
Arguments
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 |
Details
If 'mean' or 'sd' are not specified they assume the default values of '0' and '1', respectively.
Value
'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.
Author(s)
Heike Trautmann trautmann@statistik.uni-dortmund.de, Detlef Steuer steuer@hsu-hamburg.de and Olaf Mersmann olafm@p-value.net
References
B. Holland, M. Ahsanullah (1989): Further Resultson the Distribution of Meinhold and Singpurwalla, The American Statistician 43 (4), p. 216-219
Examples
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)