View source: R/family.aunivariate.R
betaprime | R Documentation |
Estimation of the two shape parameters of the beta-prime distribution by maximum likelihood estimation.
betaprime(lshape = "loglink", ishape1 = 2, ishape2 = NULL,
zero = NULL)
lshape |
Parameter link function applied to the two (positive) shape
parameters. See |
ishape1 , ishape2 , zero |
See |
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
;
these are returned as the fitted values.
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.
An object of class "vglmff"
(see vglmff-class
).
The object is used by modelling functions
such as vglm
,
rrvglm
and vgam
.
The response must have positive values only.
The beta-prime distribution is also known as the beta distribution of the second kind or the inverted beta distribution.
Thomas W. Yee
Johnson, N. L. and Kotz, S. and Balakrishnan, N. (1995). Chapter 25 of: Continuous Univariate Distributions, 2nd edition, Volume 2, New York: Wiley.
betaff
,
Beta
.
nn <- 1000
bdata <- data.frame(shape1 = exp(1), shape2 = exp(3))
bdata <- transform(bdata, yb = rbeta(nn, shape1, shape2))
bdata <- transform(bdata, y1 = (1-yb) / yb,
y2 = yb / (1-yb),
y3 = rgamma(nn, exp(3)) / rgamma(nn, exp(2)))
fit1 <- vglm(y1 ~ 1, betaprime, data = bdata, trace = TRUE)
coef(fit1, matrix = TRUE)
fit2 <- vglm(y2 ~ 1, betaprime, data = bdata, trace = TRUE)
coef(fit2, matrix = TRUE)
fit3 <- vglm(y3 ~ 1, betaprime, data = bdata, trace = TRUE)
coef(fit3, matrix = TRUE)
# Compare the fitted values
with(bdata, mean(y3))
head(fitted(fit3))
Coef(fit3) # Useful for intercept-only models
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