invgauss {statmod} | R Documentation |
Density, cumulative probability, quantiles and random generation for the inverse Gaussian distribution.
dinvgauss(x, mu, lambda=1) pinvgauss(q, mu, lambda=1) qinvgauss(p, mu, lambda=1) rinvgauss(n, mu, lambda=1)
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. |
The inverse Gaussian distribution takes values on the positive real line. The variance of the distribution is $μ^3/λ$. 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.
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.
Gordon Smyth and Dr Paul Bagshaw, Centre National d'Etudes des Telecommunications (DIH/DIPS), France (qinvgauss
)
Chhikara, R. S., and Folks, J. Leroy, (1989). The inverse Gaussian distribution: Theory, methodology, and applications. Marcel Dekker, New York.
qres
, inverse.gaussian
, .Random.seed
about random number generation.
y <- rinvgauss(20,1,2) # generate vector of 20 random numbers p <- pinvgauss(y,1,2) # p should be uniform</tt>