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.
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 $\mu^3/\lambda$. 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.
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.