Generalized Gamma Distribution
Density, distribution function, quantile function and random
generation for the generalized gamma distribution with
scale parameter scale
,
and parameters d
and k
.
dgengamma.stacy(x, scale = 1, d, k, log = FALSE) pgengamma.stacy(q, scale = 1, d, k, lower.tail = TRUE, log.p = FALSE) qgengamma.stacy(p, scale = 1, d, k, lower.tail = TRUE, log.p = FALSE) rgengamma.stacy(n, scale = 1, d, k)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations.
Same as in |
scale |
the (positive) scale parameter b. |
d, k |
the (positive) parameters d and k. Both can be thought of as shape parameters, where d is of the Weibull-type and k is of the gamma-type. |
log |
Logical.
If |
lower.tail, log.p |
See gengamma.stacy
, the VGAM family function
for estimating the generalized gamma distribution
by maximum likelihood estimation,
for formulae and other details.
Apart from n
, all the above arguments may be vectors and
are recyled to the appropriate length if necessary.
dgengamma.stacy
gives the density,
pgengamma.stacy
gives the distribution function,
qgengamma.stacy
gives the quantile function, and
rgengamma.stacy
generates random deviates.
T. W. Yee and Kai Huang
Stacy, E. W. and Mihram, G. A. (1965). Parameter estimation for a generalized gamma distribution. Technometrics, 7, 349–358.
## Not run: x <- seq(0, 14, by = 0.01); d <- 1.5; Scale <- 2; k <- 6 plot(x, dgengamma.stacy(x, Scale, d = d, k = k), type = "l", col = "blue", ylim = 0:1, main = "Blue is density, orange is cumulative distribution function", sub = "Purple are 5,10,...,95 percentiles", las = 1, ylab = "") abline(h = 0, col = "blue", lty = 2) lines(qgengamma.stacy(seq(0.05, 0.95, by = 0.05), Scale, d = d, k = k), dgengamma.stacy(qgengamma.stacy(seq(0.05, 0.95, by = 0.05), Scale, d = d, k = k), Scale, d = d, k = k), col = "purple", lty = 3, type = "h") lines(x, pgengamma.stacy(x, Scale, d = d, k = k), col = "orange") abline(h = 0, lty = 2) ## End(Not run)
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