The Inverse Gaussian Distribution
Density, distribution function and random generation for the inverse Gaussian distribution.
dinv.gaussian(x, mu, lambda, log = FALSE) pinv.gaussian(q, mu, lambda) rinv.gaussian(n, mu, lambda)
x, q |
vector of quantiles. |
n |
number of observations.
If |
mu |
the mean parameter. |
lambda |
the lambda parameter. |
log |
Logical.
If |
See inv.gaussianff, the VGAM family function
for estimating both parameters by maximum likelihood estimation,
for the formula of the probability density function.
dinv.gaussian gives the density,
pinv.gaussian gives the distribution function, and
rinv.gaussian generates random deviates.
Currently qinv.gaussian is unavailable.
T. W. Yee
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.
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.