% File src/library/stats/man/Logistic.Rd % Part of the R package, https://www.R-project.org % Copyright 1995-2014 R Core Team % Distributed under GPL 2 or later \name{Logistic} \alias{Logistic} \alias{dlogis} \alias{plogis} \alias{qlogis} \alias{rlogis} \title{The Logistic Distribution} \concept{logit} \concept{sigmoid} \description{ Density, distribution function, quantile function and random generation for the logistic distribution with parameters \code{location} and \code{scale}. } \usage{ dlogis(x, location = 0, scale = 1, log = FALSE) plogis(q, location = 0, scale = 1, lower.tail = TRUE, log.p = FALSE) qlogis(p, location = 0, scale = 1, lower.tail = TRUE, log.p = FALSE) rlogis(n, location = 0, scale = 1) } \arguments{ \item{x, q}{vector of quantiles.} \item{p}{vector of probabilities.} \item{n}{number of observations. If \code{length(n) > 1}, the length is taken to be the number required.} \item{location, scale}{location and scale parameters.} \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]}.} } \value{ \code{dlogis} gives the density, \code{plogis} gives the distribution function, \code{qlogis} gives the quantile function, and \code{rlogis} generates random deviates. The length of the result is determined by \code{n} for \code{rlogis}, and is the maximum of the lengths of the numerical arguments for the other functions. The numerical arguments other than \code{n} are recycled to the length of the result. Only the first elements of the logical arguments are used. } \details{ If \code{location} or \code{scale} are omitted, they assume the default values of \code{0} and \code{1} respectively. The Logistic distribution with \code{location} \eqn{= \mu}{= m} and \code{scale} \eqn{= \sigma}{= s} has distribution function \deqn{ F(x) = \frac{1}{1 + e^{-(x-\mu)/\sigma}}% }{F(x) = 1 / (1 + exp(-(x-m)/s))} and density \deqn{ f(x)= \frac{1}{\sigma}\frac{e^{(x-\mu)/\sigma}}{(1 + e^{(x-\mu)/\sigma})^2}% }{f(x) = 1/s exp((x-m)/s) (1 + exp((x-m)/s))^-2.} It is a long-tailed distribution with mean \eqn{\mu}{m} and variance \eqn{\pi^2/3 \sigma^2}{\pi^2 /3 s^2}. } \note{ \code{qlogis(p)} is the same as the well known \sQuote{\emph{logit}} function, \eqn{logit(p) = \log p/(1-p)}{logit(p) = log(p/(1-p))}, and \code{plogis(x)} has consequently been called the \sQuote{inverse logit}. The distribution function is a rescaled hyperbolic tangent, \code{plogis(x) == (1+ \link{tanh}(x/2))/2}, and it is called a \emph{sigmoid function} in contexts such as neural networks. } \source{ \code{[dpq]logis} are calculated directly from the definitions. \code{rlogis} uses inversion. } \references{ Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) \emph{The New S Language}. Wadsworth & Brooks/Cole. Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) \emph{Continuous Univariate Distributions}, volume 2, chapter 23. Wiley, New York. } \seealso{ \link{Distributions} for other standard distributions. } \examples{ var(rlogis(4000, 0, scale = 5)) # approximately (+/- 3) pi^2/3 * 5^2 } \keyword{distribution}