invgauss: Inverse Gaussian Distribution

Description

Density, cumulative probability, quantiles and random generation for the inverse Gaussian distribution.

Usage

dinvgauss(x, mu, lambda=1)
pinvgauss(q, mu, lambda=1)
qinvgauss(p, mu, lambda=1)
rinvgauss(n, mu, lambda=1)

Arguments

vector of quantiles. Missing values (NAs) are allowed.

vector of probabilities. Missing values (NAs) are allowed.

sample size. If length(n) is larger than 1, then length(n) random values are returned.

vector of (positive) means. This is replicated to be the same length as p or q or the number of deviates generated.

lambda

vector of (positive) precision parameters. This is replicated to be the same length as p or q or the number of deviates generated.

Value

Vector of same length as x or q giving the density (dinvgauss), probability (pinvgauss), quantile (qinvgauss) or random sample (rinvgauss) for the inverse Gaussian distribution with mean mu and inverse dispersion lambda. Elements of q or p that are missing will cause the corresponding elements of the result to be missing.

Details

The inverse Gaussian distribution takes values on the positive real line. The variance of the distribution is $^3/$. Applications of the inverse Gaussian include sequential analysis, diffusion processes and radiotechniques. The inverse Gaussian is one of the response distributions used in generalized linear models. Chhikara, R. S., and Folks, J. Leroy, (1989). The inverse Gaussian distribution: Theory, methodology, and applications. Marcel Dekker, New York. [object Object],[object Object],[object Object] qres, inverse.gaussian, .Random.seed about random number generation. y <- rinvgauss(20,1,2) # generate vector of 20 random numbers p <- pinvgauss(y,1,2) # p should be uniform distribution