
ggamma(lscale="loge", ld="loge", lk="loge",
escale=list(), ed=list(), ek=list(),
iscale=NULL, id=NULL, ik=NULL, zero=NULL)
Links
for more choices.earg
in Links
for general information."vglmff"
(see vglmff-class
).
The object is used by modelling functions such as vglm
,
and vgam
.There are many special cases, as given in Table 1 of Stacey and Mihram (1965). In the following, the parameters are in the order $b,d,k$. The special cases are: Exponential $f(y;b,1,1)$, Gamma $f(y;b,1,k)$, Weibull $f(y;b,d,1)$, Chi Squared $f(y;2,1,a/2)$ with $a$ degrees of freedom, Chi $f(y;\sqrt{2},2,a/2)$ with $a$ degrees of freedom, Half-normal $f(y;\sqrt{2},2,1/2)$, Circular normal $f(y;\sqrt{2},2,1)$, Spherical normal $f(y;\sqrt{2},2,3/2)$, Rayleigh $f(y;c\sqrt{2},2,1)$ where $c>0$.
Stacy, E. W. and Mihram, G. A. (1965) Parameter estimation for a generalized gamma distribution. Technometrics, 7, 349--358.
Prentice, R. L. (1974) A log gamma model and its maximum likelihood estimation. Biometrika, 61, 539--544.
rggamma
,
gamma1
,
gamma2
,
prentice74
.k = exp(-1)
Scale = exp(1)
y = rgamma(n=1000, shape=k, scale=Scale)
fit = vglm(y ~ 1, ggamma, trace=TRUE)
coef(fit, matrix=TRUE)
# Another example
x = runif(n <- 5000)
Scale = exp(1)
d = exp(0 + 1.2*x)
k = exp(-1 + 2*x)
y = rggamma(n, scale=Scale, d=d, k=k)
fit = vglm(y ~ x, ggamma(zero=1, iscal=6), trace=TRUE)
coef(fit, matrix=TRUE)
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