VGAM (version 0.7-2)

Inv.gaussian: The Inverse Gaussian Distribution

Description

Density, distribution function and random generation for the inverse Gaussian distribution.

Usage

dinv.gaussian(x, mu, lambda)
pinv.gaussian(q, mu, lambda)
rinv.gaussian(n, mu, lambda)

Arguments

x, q

vector of quantiles.

n

number of observations. Must be a single positive integer.

mu

the mean parameter.

lambda

the $\lambda$ parameter.

Value

• dinv.gaussian gives the density, pinv.gaussian gives the distribution function, and rinv.gaussian generates random deviates.

Details

See inv.gaussianff, the VGAM family function for estimating both parameters by maximum likelihood estimation, for the formula of the probability density function.

References

Johnson, N. L. and Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, 2nd edition, Volume 1, New York: Wiley.

Taraldsen, G. and Lindqvist, B. H. (2005) The multiple roots simulation algorithm, the inverse Gaussian distribution, and the sufficient conditional Monte Carlo method. Preprint Statistics No. 4/2005, Norwegian University of Science and Technology, Trondheim, Norway.

See Also

inv.gaussianff.

Examples

x = seq(-0.05, 4, len=300)
plot(x, dinv.gaussian(x, mu=1, lambda=1), type="l", col="blue", las=1,
     main="blue is density, red is cumulative distribution function")
abline(h=0, col="blue", lty=2)
lines(x, pinv.gaussian(x, mu=1, lambda=1), type="l", col="red")