# NormalLaplace: Normal Laplace Distribution In NormalLaplace: The Normal Laplace Distribution

## Description

Density function, distribution function, quantiles and random number generation for the normal Laplace distribution, with parameters mu (location), delta (scale), beta (skewness), and nu (shape).

## Usage

 1 2 3 4 5 6 7 8 9 dnl(x, mu = 0, sigma = 1, alpha = 1, beta = 1, param = c(mu,sigma,alpha,beta)) pnl(q, mu = 0, sigma = 1, alpha = 1, beta = 1, param = c(mu,sigma,alpha,beta)) qnl(p, mu = 0, sigma = 1, alpha = 1, beta = 1, param = c(mu,sigma,alpha,beta), tol = 10^(-5), nInterpol = 100, subdivisions = 100, ...) rnl(n, mu = 0, sigma = 1, alpha = 1, beta = 1, param = c(mu,sigma,alpha,beta))

## Arguments

 x, q Vector of quantiles. p Vector of probabilities. n Number of random variates to be generated. mu Location parameter mu, default is 0. sigma Scale parameter sigma, default is 1. alpha Skewness parameter alpha, default is 1. beta Shape parameter beta, default is 1. param Specifying the parameters as a vector of the form c(mu,sigma,alpha,beta). tol Specified level of tolerance when checking if parameter beta is equal to 0. subdivisions The maximum number of subdivisions used to integrate the density and determine the accuracy of the distribution function calculation. nInterpol Number of points used in qnl for cubic spline interpolation of the distribution function. ... Passes arguments to uniroot.

## Details

Users may either specify the values of the parameters individually or as a vector. If both forms are specified, then the values specified by the vector param will overwrite the other ones.

The density function is

f(y) = alpha beta/(alpha + beta)phi((y - mu)/sigma)[ R(alpha sigma - (y - mu)/sigma) + R(beta sigma + (y - mu)/sigma)

The distribution function is

F(y)=Phi((y-mu)/sigma) - phi((y-mu)/sigma)[beta R(alpha sigma - (y-mu)/sigma) - R(beta sigma + (y-mu)/sigma)]/(alpha + beta)

The function R(z) is the Mills' Ratio, see millsR.

Generation of random observations from the normal Laplace distribution using rnl is based on the representation

Y ~ Z + W

where Z and W are independent random variables with

Z ~ N(mu, sigma^2)

and W following an asymmetric Laplace distribution with pdf

(alpha beta)/(alpha + beta)e^{beta w} for w <= 0 and (alpha beta)/(alpha + beta)e^{-beta w} for w > 0

## Value

dnl gives the density function, pnl gives the distribution function, qnl gives the quantile function and rnl generates random variates.

## Author(s)

David Scott [email protected], Jason Shicong Fu

## References

William J. Reed. (2006) The Normal-Laplace Distribution and Its Relatives. In Advances in Distribution Theory, Order Statistics and Inference, pp. 61–74. Birkh<e4>user, Boston.

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 param <- c(0,1,3,2) par(mfrow = c(1,2)) ## Curves of density and distribution curve(dnl(x, param = param), -5, 5, n = 1000) title("Density of the Normal Laplace Distribution") curve(pnl(x, param = param), -5, 5, n = 1000) title("Distribution Function of the Normal Laplace Distribution") ## Example of density and random numbers par(mfrow = c(1,1)) param1 <- c(0,1,1,1) data1 <- rnl(1000, param = param1) curve(dnl(x, param = param1), from = -5, to = 5, n = 1000, col = 2) hist(data1, freq = FALSE, add = TRUE) title("Density and Histogram")

NormalLaplace documentation built on May 29, 2017, 12:23 p.m.