Lognormal
The Log Normal Distribution
Description
Density, distribution function, quantile function and random generation for the log normal distribution whose logarithm has mean equal to meanlog
and standard deviation equal to sdlog
.
Usage
dlnorm(x, meanlog = 0, sdlog = 1, log = FALSE) plnorm(q, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE) qlnorm(p, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE) rlnorm(n, meanlog = 0, sdlog = 1)
Arguments
x, q | vector of quantiles. |
p | vector of probabilities. |
n | number of observations. If |
meanlog, sdlog | mean and standard deviation of the distribution on the log scale with default values of |
log, log.p | logical; if TRUE, probabilities p are given as log(p). |
lower.tail | logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x]. |
Details
The log normal distribution has density
f(x) = 1/(√(2 π) σ x) e^-((log x - μ)^2 / (2 σ^2))
where μ and σ are the mean and standard deviation of the logarithm. The mean is E(X) = exp(μ + 1/2 σ^2), the median is med(X) = exp(μ), and the variance Var(X) = exp(2*μ + σ^2)*(exp(σ^2) - 1) and hence the coefficient of variation is sqrt(exp(σ^2) - 1) which is approximately σ when that is small (e.g., σ < 1/2).
Value
dlnorm
gives the density, plnorm
gives the distribution function, qlnorm
gives the quantile function, and rlnorm
generates random deviates.
The length of the result is determined by n
for rlnorm
, and is the maximum of the lengths of the numerical arguments for the other functions.
The numerical arguments other than n
are recycled to the length of the result. Only the first elements of the logical arguments are used.
Note
The cumulative hazard H(t) = - log(1 - F(t)) is -plnorm(t, r, lower = FALSE, log = TRUE)
.
Source
dlnorm
is calculated from the definition (in ‘Details’). [pqr]lnorm
are based on the relationship to the normal.
Consequently, they model a single point mass at exp(meanlog)
for the boundary case sdlog = 0
.
References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, volume 1, chapter 14. Wiley, New York.
See Also
Distributions for other standard distributions, including dnorm
for the normal distribution.
Examples
dlnorm(1) == dnorm(0)
Copyright (©) 1999–2012 R Foundation for Statistical Computing.
Licensed under the GNU General Public License.