dagum(link.a = "loge", link.scale = "loge", link.p = "loge",
earg.a=list(), earg.scale=list(), earg.p=list(),
init.a = NULL, init.scale = NULL, init.p = 1, zero = NULL)
a
, scale
, and p
.
See Links
for more choices.earg
in Links
for general information.a
, scale
, and p
.a
, scale
, p
, respectively."vglmff"
(see vglmff-class
).
The object is used by modelling functions such as vglm
,
and vgam
.
The Dagum distribution has a cumulative distribution function
$$F(y) = [1 + (y/b)^{-a}]^{-p}$$
which leads to a probability density function
$$f(y) = ap y^{ap-1} / [b^{ap} {1 + (y/b)^a}^{p+1}]$$
for $a > 0$, $b > 0$, $p > 0$, $y > 0$.
Here, $b$ is the scale parameter scale
,
and the others are shape parameters.
The mean is
$$E(Y) = b \, \Gamma(p + 1/a) \, \Gamma(1 - 1/a) / \Gamma(p)$$
provided $-ap < 1 < a$.
Dagum
,
genbetaII
,
betaII
,
sinmad
,
fisk
,
invlomax
,
lomax
,
paralogistic
,
invparalogistic
.y = rdagum(n=3000, 4, 6, 2)
fit = vglm(y ~ 1, dagum, trace=TRUE)
fit = vglm(y ~ 1, dagum(init.a=2.1), trace=TRUE, crit="c")
coef(fit, mat=TRUE)
Coef(fit)
summary(fit)
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