betaprime: The Beta-Prime Distribution

Description

Estimation of the two shape parameters of the beta-prime distribution by maximum likelihood estimation.

Usage

betaprime(link = "loge", i1 = 2, i2 = NULL, zero = NULL)

Arguments

link

Parameter link function applied to the two (positive) shape parameters. See Links for more choices.

i1, i2

Initial values for the first and second shape parameters. A NULL value means it is obtained in the initialize slot. Note that i2 is obtained using i1.

zero

An integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. The value must be from the set {1,2} corresponding respectively to shape1 and shape2 respectively. If zero=NULL

`Value`

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

`Details`

The beta-prime distribution is given by
  $$f(y) = y^{shape1-1} (1+y)^{-shape1-shape2} /
  B(shape1,shape2)$$
  for $y > 0$.
  The shape parameters are positive, and
  here, $B$ is the beta function.
  The mean of $Y$ is $shape1 / (shape2-1)$ provided $shape2>1$.
  If $Y$ has a $Beta(shape1,shape2)$ distribution then
  $Y/(1-Y)$ and $(1-Y)/Y$ have a $Betaprime(shape1,shape2)$
  and $Betaprime(shape2,shape1)$ distribution respectively.
  Also, if $Y_1$ has a $gamma(shape1)$ distribution
  and $Y_2$ has a $gamma(shape2)$ distribution
  then $Y_1/Y_2$ has a $Betaprime(shape1,shape2)$
  distribution.

`References`

Johnson, N. L. and Kotz, S. and Balakrishnan, N. (1995) 
Chapter 25 of: 
Continuous Univariate Distributions,
2nd edition,
Volume 2,
New York: Wiley.
Documentation accompanying the VGAM package at
http://www.stat.auckland.ac.nz/~yee
contains further information and examples.

`See Also`

betaff.

`Examples`

Run this codeyb = rbeta(n <- 1000, shape1=exp(1), shape2=exp(3))
y1 = (1-yb)/yb
y2 = yb/(1-yb)
y3 = rgamma(n, exp(3)) / rgamma(n, exp(2))

fit1 = vglm(y1 ~ 1, betaprime, trace=TRUE)
coef(fit1, matrix=TRUE)

fit2 = vglm(y2 ~ 1, betaprime, trace=TRUE)
coef(fit2, matrix=TRUE)

fit3 = vglm(y3 ~ 1, betaprime, trace=TRUE)
coef(fit3, matrix=TRUE)

# Compare the fitted values
mean(y3)
fitted(fit3)[1:5]
Coef(fit3)  # Useful for intercept-only models
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