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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/truncated-normal-distribution.R
\name{TruncNormal}
\alias{TruncNormal}
\alias{dtnorm}
\alias{ptnorm}
\alias{qtnorm}
\alias{rtnorm}
\title{Truncated normal distribution}
\usage{
dtnorm(x, mean = 0, sd = 1, a = -Inf, b = Inf, log = FALSE)
ptnorm(
q,
mean = 0,
sd = 1,
a = -Inf,
b = Inf,
lower.tail = TRUE,
log.p = FALSE
)
qtnorm(
p,
mean = 0,
sd = 1,
a = -Inf,
b = Inf,
lower.tail = TRUE,
log.p = FALSE
)
rtnorm(n, mean = 0, sd = 1, a = -Inf, b = Inf)
}
\arguments{
\item{x, q}{vector of quantiles.}
\item{mean, sd}{location and scale parameters. Scale must be positive.}
\item{a, b}{lower and upper truncation points (\code{a < x <= b},
with \code{a = -Inf} and \code{b = Inf} by default).}
\item{log, log.p}{logical; if TRUE, probabilities p are given as log(p).}
\item{lower.tail}{logical; if TRUE (default), probabilities are \eqn{P[X \le x]}
otherwise, \eqn{P[X > x]}.}
\item{p}{vector of probabilities.}
\item{n}{number of observations. If \code{length(n) > 1},
the length is taken to be the number required.}
}
\description{
Density, distribution function, quantile function and random generation
for the truncated normal distribution.
}
\details{
Probability density function
\deqn{
f(x) = \frac{\phi(\frac{x-\mu}{\sigma})}
{\Phi(\frac{b-\mu}{\sigma}) - \Phi(\frac{a-\mu}{\sigma})}
}{
f(x) = \phi((x-\mu)/\sigma) / (\Phi((b-\mu)/\sigma) - \Phi((a-\mu)/\sigma))
}
Cumulative distribution function
\deqn{
F(x) = \frac{\Phi(\frac{x-\mu}{\sigma}) - \Phi(\frac{a-\mu}{\sigma})}
{\Phi(\frac{b-\mu}{\sigma}) - \Phi(\frac{a-\mu}{\sigma})}
}{
F(x) = (\Phi((x-\mu)/\sigma) - \Phi((a-\mu)/\sigma)) / (\Phi((b-\mu)/\sigma) - \Phi((a-\mu)/\sigma))
}
Quantile function
\deqn{
F^{-1}(p) = \Phi^{-1}\left(\Phi\left(\frac{a-\mu}{\sigma}\right) + p \times
\left[\Phi\left(\frac{b-\mu}{\sigma}\right) -
\Phi\left(\frac{a-\mu}{\sigma}\right)\right]\right)
}{
F^-1(p) = \Phi^-1(\Phi((a-\mu)/\sigma) + p * (\Phi((b-\mu)/\sigma) - \Phi((a-\mu)/\sigma)))
}
For random generation algorithm described by Robert (1995) is used.
}
\examples{
x <- rtnorm(1e5, 5, 3, b = 7)
hist(x, 100, freq = FALSE)
curve(dtnorm(x, 5, 3, b = 7), -8, 8, col = "red", add = TRUE)
hist(ptnorm(x, 5, 3, b = 7))
plot(ecdf(x))
curve(ptnorm(x, 5, 3, b = 7), -8, 8, col = "red", lwd = 2, add = TRUE)
R <- 1e5
partmp <- par(mfrow = c(2,4), mar = c(2,2,2,2))
hist(rtnorm(R), freq= FALSE, main = "", xlab = "", ylab = "")
curve(dtnorm(x), -5, 5, col = "red", add = TRUE)
hist(rtnorm(R, a = 0), freq= FALSE, main = "", xlab = "", ylab = "")
curve(dtnorm(x, a = 0), -1, 5, col = "red", add = TRUE)
hist(rtnorm(R, b = 0), freq= FALSE, main = "", xlab = "", ylab = "")
curve(dtnorm(x, b = 0), -5, 5, col = "red", add = TRUE)
hist(rtnorm(R, a = 0, b = 1), freq= FALSE, main = "", xlab = "", ylab = "")
curve(dtnorm(x, a = 0, b = 1), -1, 2, col = "red", add = TRUE)
hist(rtnorm(R, a = -1, b = 0), freq= FALSE, main = "", xlab = "", ylab = "")
curve(dtnorm(x, a = -1, b = 0), -2, 2, col = "red", add = TRUE)
hist(rtnorm(R, mean = -6, a = 0), freq= FALSE, main = "", xlab = "", ylab = "")
curve(dtnorm(x, mean = -6, a = 0), -2, 1, col = "red", add = TRUE)
hist(rtnorm(R, mean = 8, b = 0), freq= FALSE, main = "", xlab = "", ylab = "")
curve(dtnorm(x, mean = 8, b = 0), -2, 1, col = "red", add = TRUE)
hist(rtnorm(R, a = 3, b = 5), freq= FALSE, main = "", xlab = "", ylab = "")
curve(dtnorm(x, a = 3, b = 5), 2, 5, col = "red", add = TRUE)
par(partmp)
}
\references{
Robert, C.P. (1995). Simulation of truncated normal variables.
Statistics and Computing 5(2): 121-125. \url{https://arxiv.org/abs/0907.4010}
Burkardt, J. (17 October 2014). The Truncated Normal Distribution. Florida State University.
\url{https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf}
}
\concept{Continuous}
\concept{Univariate}
\keyword{distribution}
|
