invgauss

0th

Percentile

Inverse Gaussian Distribution

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

Keywords
distribution
Usage
dinvgauss(x, mu, lambda=1)
pinvgauss(q, mu, lambda=1)
qinvgauss(p, mu, lambda=1)
rinvgauss(n, mu, lambda=1)
Arguments
x
vector of quantiles. Missing values (NAs) are allowed.
q
vector of quantiles. Missing values (NAs) are allowed.
p
vector of probabilities. Missing values (NAs) are allowed.
n
sample size. If length(n) is larger than 1, then length(n) random values are returned.
mu
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.
Details

The inverse Gaussian distribution takes values on the positive real line. The variance of the distribution is $\mu^3/\lambda$. 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.

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.

References

Chhikara, R. S., and Folks, J. Leroy, (1989). The inverse Gaussian distribution: Theory, methodology, and applications. Marcel Dekker, New York.

See Also

dinvGauss, pinvGauss, qinvGauss and rinvGauss in the SuppDists package.

Aliases
  • InverseGaussian
  • dinvgauss
  • pinvgauss
  • qinvgauss
  • rinvgauss
Examples
y <- rinvgauss(20,1,2) # generate vector of 20 random numbers
p <- pinvgauss(y,1,2) # p should be uniform
Documentation reproduced from package statmod, version 1.2.0, License: LGPL version 2 or newer

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