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VGAM (version 0.7-5)

fisk: Fisk Distribution family function

Description

Maximum likelihood estimation of the 2-parameter Fisk distribution.

Usage

fisk(link.a = "loge", link.scale = "loge",
     earg.a=list(), earg.scale=list(),
     init.a = NULL, init.scale = NULL, zero = NULL)

Arguments

link.a, link.scale
Parameter link functions applied to the (positive) parameters a and scale. See Links for more choices.
earg.a, earg.scale
List. Extra argument for each of the links. See earg in Links for general information.
init.a, init.scale
Optional initial values for a and scale.
zero
An integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. Here, the values must be from the set {1,2} which correspond to a, scale, respectively.

Value

  • An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, and vgam.

Details

The 2-parameter Fisk (aka log-logistic) distribution is the 4-parameter generalized beta II distribution with shape parameter $q=p=1$. It is also the 3-parameter Singh-Maddala distribution with shape parameter $q=1$, as well as the Dagum distribution with $p=1$. More details can be found in Kleiber and Kotz (2003).

The Fisk distribution has density $$f(y) = a y^{a-1} / [b^a {1 + (y/b)^a}^2]$$ for $a > 0$, $b > 0$, $y > 0$. Here, $b$ is the scale parameter scale, and a is a shape parameter. The cumulative distribution function is $$F(y) = 1 - [1 + (y/b)^a]^{-1} = [1 + (y/b)^{-a}]^{-1}.$$ The mean is $$E(Y) = b \, \Gamma(1 + 1/a) \, \Gamma(1 - 1/a)$$ provided $a > 1$.

References

Kleiber, C. and Kotz, S. (2003) Statistical Size Distributions in Economics and Actuarial Sciences, Hoboken, NJ: Wiley-Interscience.

See Also

Fisk, genbetaII, betaII, dagum, sinmad, invlomax, lomax, paralogistic, invparalogistic.

Examples

Run this code
y = rfisk(n=200, 4, 6)
fit = vglm(y ~ 1, fisk, trace=TRUE)
fit = vglm(y ~ 1, fisk(init.a=3.3), trace=TRUE, crit="c")
coef(fit, mat=TRUE)
Coef(fit)
summary(fit)

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