| tlnorm | R Documentation | 
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  | 
| lower.tail | Logical indicating if the probabilities are of the form  | 
| log.p | Logical indicating if the probabilities are given as  | 
The Cumulative Distribution Function (CDF) of the truncated log-normal distribution is equal to
F_T(x) = F(x) / F(T) for x \le 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.
Lognormal,  Distributions
# 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")
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