R: Generalized gamma distribution (original parameterisation)

GenGamma.orig {flexsurv} | R Documentation

## Generalized gamma distribution (original parameterisation)

### Description

Density, distribution function, hazards, quantile function and random generation for the generalized gamma distribution, using the original parameterisation from Stacy (1962).

### Usage

    dgengamma.orig(x, shape, scale = 1, k, log = FALSE)

    pgengamma.orig(q, shape, scale = 1, k, lower.tail = TRUE, log.p = FALSE)

    Hgengamma.orig(x, shape, scale = 1, k)

    hgengamma.orig(x, shape, scale = 1, k)

    qgengamma.orig(p, shape, scale = 1, k, lower.tail = TRUE, log.p = FALSE)

    rgengamma.orig(n, shape, scale = 1, k)

### Arguments

`x, q` | vector of quantiles.
`shape` | vector of “Weibull” shape parameters.
`scale` | vector of scale parameters.
`k` | vector of “Gamma” shape parameters.
`log, log.p` | logical; if TRUE, probabilities p are given as log(p).
`lower.tail` | logical; if TRUE (default), probabilities are P(X <= x), otherwise, P(X > x).
`p` | vector of probabilities.
`n` | number of observations. If `length(n) > 1`, the length is taken to be the number required.

### Details

If w ~ Gamma(k, 1), then x = exp(w/shape + log(scale)) follows the original generalised gamma distribution with the parameterisation given here (Stacy 1962). Defining `shape`=b>0, `scale`=a>0, x has probability density

f(x | a, b, k) = (b / Γ(k)) (x^{bk -1} / a^{bk}) exp(-(x/a)^b)

f(x | a, b, k) = (b / Γ(k)) (x^{bk -1} / a^{bk}) exp(-(x/a)^b)

The original generalized gamma distribution simplifies to the gamma, exponential and Weibull distributions with the following parameterisations:

`dgengamma.orig(x, shape, scale, k=1)` | `=` | `dweibull(x, shape, scale)`
`dgengamma.orig(x, shape=1, scale, k)` | `=` | `dgamma(x, shape=k, scale)`
`dgengamma.orig(x, shape=1, scale, k=1)` | `=` | `dexp(x, rate=1/scale)`

Also as k tends to infinity, it tends to the log normal (as in `dlnorm`) with the following parameters (Lawless, 1980):

`dlnorm(x, meanlog=log(scale) + log(k)/shape, sdlog=1/(shape*sqrt(k)))`

For more stable behaviour as the distribution tends to the log-normal, an alternative parameterisation was developed by Prentice (1974). This is given in `dgengamma`, and is now preferred for statistical modelling. It is also more flexible, including a further new class of distributions with negative shape `k`.

The generalized F distribution `GenF.orig`, and its similar alternative parameterisation `GenF`, extend the generalized gamma to four parameters.

### Value

`dgengamma.orig` gives the density, `pgengamma.orig` gives the distribution function, `qgengamma.orig` gives the quantile function, `rgengamma.orig` generates random deviates, `Hgengamma.orig` retuns the cumulative hazard and `hgengamma.orig` the hazard.

### Author(s)

Christopher Jackson <chris.jackson@mrc-bsu.cam.ac.uk>

### References

Stacy, E. W. (1962). A generalization of the gamma distribution. Annals of Mathematical Statistics 33:1187-92.

Prentice, R. L. (1974). A log gamma model and its maximum likelihood estimation. Biometrika 61(3):539-544.

Lawless, J. F. (1980). Inference in the generalized gamma and log gamma distributions. Technometrics 22(3):409-419.

### See Also

`GenGamma`, `GenF.orig`, `GenF`, `Lognormal`, `GammaDist`, `Weibull`.

[Package flexsurv version 2.1 Index]