The truncated log-normal distribution
Density, distribution function, quantile function and random generation for the truncated log-normal distribution.
dtlnorm(x, meanlog = 0, sdlog = 1, endpoint = Inf, log = FALSE) ptlnorm(x, meanlog = 0, sdlog = 1, endpoint = Inf, lower.tail = TRUE, log.p = FALSE) qtlnorm(p, meanlog = 0, sdlog = 1, endpoint = Inf, lower.tail = TRUE, log.p = FALSE) rtlnorm(n, meanlog = 0, sdlog = 1, endpoint = Inf)
x |
Vector of quantiles. |
p |
Vector of probabilities. |
n |
Number of observations. |
meanlog |
Mean of the distribution on the log scale, default is 0. |
sdlog |
Standard deviation of the distribution on the log scale, default is 1. |
endpoint |
Endpoint of the truncated log-normal distribution. The default value is |
log |
Logical indicating if the densities are given as \log(f), default is |
lower.tail |
Logical indicating if the probabilities are of the form P(X≤ x) ( |
log.p |
Logical indicating if the probabilities are given as \log(p), default is |
The Cumulative Distribution Function (CDF) of the truncated log-normal distribution is equal to F_T(x) = F(x) / F(T) for x ≤ T where F is the CDF of the ordinary log-normal distribution and T is the endpoint (truncation point) of the truncated log-normal distribution.
dtlnorm gives the density function evaluated in x, ptlnorm the CDF evaluated in x and qtlnorm the quantile function evaluated in p. The length of the result is equal to the length of x or p.
rtlnorm returns a random sample of length n.
Tom Reynkens.
# Plot of the PDF x <- seq(0, 10, 0.01) plot(x, dtlnorm(x, endpoint=9), xlab="x", ylab="PDF", type="l") # Plot of the CDF x <- seq(0, 10, 0.01) plot(x, ptlnorm(x, endpoint=9), xlab="x", ylab="CDF", type="l")
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.