# gammahyperbola: Gamma Hyperbola Bivariate Distribution In VGAM: Vector Generalized Linear and Additive Models

 gammahyperbola R Documentation

## Gamma Hyperbola Bivariate Distribution

### Description

Estimate the parameter of a gamma hyperbola bivariate distribution by maximum likelihood estimation.

### Usage

gammahyperbola(ltheta = "loglink", itheta = NULL, expected = FALSE)


### Arguments

 ltheta Link function applied to the (positive) parameter \theta. See Links for more choices. itheta Initial value for the parameter. The default is to estimate it internally. expected Logical. FALSE means the Newton-Raphson (using the observed information matrix) algorithm, otherwise the expected information matrix is used (Fisher scoring algorithm).

### Details

The joint probability density function is given by

f(y_1,y_2) = \exp( -e^{-\theta} y_1 / \theta - \theta y_2 )

for \theta > 0, y_1 > 0, y_2 > 1. The random variables Y_1 and Y_2 are independent. The marginal distribution of Y_1 is an exponential distribution with rate parameter \exp(-\theta)/\theta. The marginal distribution of Y_2 is an exponential distribution that has been shifted to the right by 1 and with rate parameter \theta. The fitted values are stored in a two-column matrix with the marginal means, which are \theta \exp(\theta) and 1 + 1/\theta.

The default algorithm is Newton-Raphson because Fisher scoring tends to be much slower for this distribution.

### Value

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

### Note

The response must be a two-column matrix.

T. W. Yee

### References

Reid, N. (2003). Asymptotics and the theory of inference. Annals of Statistics, 31, 1695–1731.

exponential.

### Examples

gdata <- data.frame(x2 = runif(nn <- 1000))
gdata <- transform(gdata, theta = exp(-2 + x2))
gdata <- transform(gdata, y1 = rexp(nn, rate = exp(-theta)/theta),
y2 = rexp(nn, rate = theta) + 1)
fit <- vglm(cbind(y1, y2) ~ x2, gammahyperbola(expected = TRUE), data = gdata)
coef(fit, matrix = TRUE)
Coef(fit)