Inv.gaussian: The Inverse Gaussian Distribution

In VGAM: Vector Generalized Linear and Additive Models

The Inverse Gaussian Distribution

Description

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

Usage

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

Arguments

x, q	vector of quantiles.
n	number of observations. If length(n) > 1 then the length is taken to be the number required.
mu	the mean parameter.
lambda	the \lambda parameter.
log	Logical. If log = TRUE then the logarithm of the density is returned.

Details

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

Value

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

Note

Currently qinv.gaussian is unavailable.

Author(s)

T. W. Yee

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, waldff.

Examples

## Not run:  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, orange is cumulative distribution function")
abline(h = 0, col = "gray", lty = 2)
lines(x, pinv.gaussian(x, mu = 1, lambda = 1), type = "l", col = "orange") 
## End(Not run)

VGAM documentation built on Sept. 18, 2024, 9:09 a.m.