LaplacesDemon: dist.Log.Normal.Precision – R documentation

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dist.Log.Normal.Precision

Log-Normal Distribution: Precision Parameterization

Description

These functions provide the density, distribution function, quantile function, and random generation for the univariate log-normal distribution with mean mu and precision tau.

Usage

dlnormp(x, mu, tau=NULL, var=NULL, log=FALSE)
plnormp(q, mu, tau, lower.tail=TRUE, log.p=FALSE)
qlnormp(p, mu, tau, lower.tail=TRUE, log.p=FALSE)
rlnormp(n, mu, tau=NULL, var=NULL)

Arguments

`x, q`	These are each a vector of quantiles.
`p`	This is a vector of probabilities.
`n`	This is the number of observations, which must be a positive integer that has length 1.
`mu`	This is the mean parameter mu.
`tau`	This is the precision parameter tau, which must be positive. Tau and var cannot be used together
`var`	This is the variance parameter, which must be positive. Tau and var cannot be used together
`log, log.p`	Logical. If `TRUE`, then probabilities p are given as log(p).
`lower.tail`	Logical. If `TRUE` (default), then probabilities are Pr[X <= x], otherwise, Pr[X > x].

Details

Application: Continuous Univariate
Density: p(theta) = sqrt(tau/(2*pi)) * (1/theta) * exp(-(tau/2)*(log(theta-mu))^2)
Inventor: Carl Friedrich Gauss or Abraham De Moivre
Notation 1: theta ~ Log-N(mu, tau^(-1))
Notation 2: p(theta) = Log-N(theta | mu, tau^(-1))
Parameter 1: mean parameter mu
Parameter 2: precision parameter tau > 0
Mean: E(theta) = exp(mu + tau^(-1) / 2)
Variance: var(theta) = exp(tau^(-1) - 1) * exp(2*mu + tau^(-1))
Mode: mode(theta) = exp(mu - tau^(-1))

The log-normal distribution, also called the Galton distribution, is applied to a variable whose logarithm is normally-distributed. The distribution is usually parameterized with mean and variance, or in Bayesian inference, with mean and precision, where precision is the inverse of the variance. In contrast, Base R parameterizes the log-normal distribution with the mean and standard deviation. These functions provide the precision parameterization for convenience and familiarity.

A flat distribution is obtained in the limit as tau -> 0.

These functions are similar to those in base R.

Value

dlnormp gives the density, plnormp gives the distribution function, qlnormp gives the quantile function, and rlnormp generates random deviates.

Author(s)

Statisticat, LLC. software@bayesian-inference.com

Examples

library(LaplacesDemon)
x <- dlnormp(1,0,1)
x <- plnormp(1,0,1)
x <- qlnormp(0.5,0,1)
x <- rlnormp(100,0,1)

#Plot Probability Functions
x <- seq(from=0.1, to=3, by=0.01)
plot(x, dlnormp(x,0,0.1), ylim=c(0,1), type="l", main="Probability Function",
     ylab="density", col="red")
lines(x, dlnormp(x,0,1), type="l", col="green")
lines(x, dlnormp(x,0,5), type="l", col="blue")
legend(2, 0.9, expression(paste(mu==0, ", ", tau==0.1),
     paste(mu==0, ", ", tau==1), paste(mu==0, ", ", tau==5)),
     lty=c(1,1,1), col=c("red","green","blue"))

LaplacesDemon

Complete Environment for Bayesian Inference

v16.1.4

MIT + file LICENSE

Authors

Byron Hall [aut], Martina Hall [aut], Statisticat, LLC [aut], Eric Brown [ctb], Richard Hermanson [ctb], Emmanuel Charpentier [ctb], Daniel Heck [ctb], Stephane Laurent [ctb], Quentin F. Gronau [ctb], Henrik Singmann [cre]

Initial release