Lognormal {stats} | R Documentation |
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
.
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)
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
|
The log normal distribution has density
f(x) = \frac{1}{\sqrt{2\pi}\sigma x} e^{-(\log(x) - \mu)^2/2 \sigma^2}%
where \mu
and \sigma
are the mean and standard
deviation of the logarithm.
The mean is E(X) = exp(\mu + 1/2 \sigma^2)
,
the median is med(X) = exp(\mu)
, and the variance
Var(X) = exp(2\mu + \sigma^2)(exp(\sigma^2) - 1)
and
hence the coefficient of variation is
\sqrt{exp(\sigma^2) - 1}
which is
approximately \sigma
when that is small (e.g., \sigma < 1/2
).
dlnorm
gives the density,
plnorm
gives the distribution function,
qlnorm
gives the quantile function, and
rlnorm
generates random deviates.
The cumulative hazard H(t) = - \log(1 - F(t))
is -plnorm(t, r, lower = FALSE, log = TRUE)
.
dlnorm
is calculated from the definition (in ‘Details’).
[pqr]lnorm
are based on the relationship to the normal.
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.
dnorm
for the normal distribution.
dlnorm(1) == dnorm(0)