gamma2: 2-parameter Gamma Regression Family Function

View source: R/family.univariate.R

gamma2R Documentation

2-parameter Gamma Regression Family Function


Estimates the 2-parameter gamma distribution by maximum likelihood estimation.


gamma2(lmu = "loglink", lshape = "loglink", imethod = 1,  ishape = NULL,
       parallel = FALSE, deviance.arg = FALSE, zero = "shape")


lmu, lshape

Link functions applied to the (positive) mu and shape parameters (called \mu and a respectively). See Links for more choices.


Optional initial value for shape. A NULL means a value is computed internally. If a failure to converge occurs, try using this argument. This argument is ignored if used within cqo; see the iShape argument of qrrvglm.control instead.


An integer with value 1 or 2 which specifies the initialization method for the \mu parameter. If failure to converge occurs try another value (and/or specify a value for ishape).


Logical. If TRUE, the deviance function is attached to the object. Under ordinary circumstances, it should be left alone because it really assumes the shape parameter is at the maximum likelihood estimate. Consequently, one cannot use that criterion to minimize within the IRLS algorithm. It should be set TRUE only when used with cqo under the fast algorithm.


See CommonVGAMffArguments for information.


Details at CommonVGAMffArguments. If parallel = TRUE then the constraint is not applied to the intercept.


This distribution can model continuous skewed responses. The density function is given by

f(y;\mu,a) = \frac{\exp(-a y / \mu) \times (a y / \mu)^{a-1} \times a}{ \mu \times \Gamma(a)}

for \mu > 0, a > 0 and y > 0. Here, \Gamma(\cdot) is the gamma function, as in gamma. The mean of Y is \mu=\mu (returned as the fitted values) with variance \sigma^2 = \mu^2 / a. If 0<a<1 then the density has a pole at the origin and decreases monotonically as y increases. If a=1 then this corresponds to the exponential distribution. If a>1 then the density is zero at the origin and is unimodal with mode at y = \mu - \mu / a; this can be achieved with lshape="logloglink".

By default, the two linear/additive predictors are \eta_1=\log(\mu) and \eta_2=\log(a). This family function implements Fisher scoring and the working weight matrices are diagonal.

This VGAM family function handles multivariate responses, so that a matrix can be used as the response. The number of columns is the number of species, say, and zero=-2 means that all species have a shape parameter equalling a (different) intercept only.


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


The response must be strictly positive. A moment estimator for the shape parameter may be implemented in the future.

If mu and shape are vectors, then rgamma(n = n, shape = shape, scale = mu/shape) will generate random gamma variates of this parameterization, etc.; see GammaDist.


T. W. Yee


The parameterization of this VGAM family function is the 2-parameter gamma distribution described in the monograph

McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2nd ed. London: Chapman & Hall.

See Also

gamma1 for the 1-parameter gamma distribution, gammaR for another parameterization of the 2-parameter gamma distribution that is directly matched with rgamma, bigamma.mckay for a bivariate gamma distribution, for another, expexpff, GammaDist, gordlink, CommonVGAMffArguments, simulate.vlm, negloglink.


# Essentially a 1-parameter gamma
gdata <- data.frame(y = rgamma(n = 100, shape = exp(1)))
fit1 <- vglm(y ~ 1, gamma1, data = gdata)
fit2 <- vglm(y ~ 1, gamma2, data = gdata, trace = TRUE, crit = "coef")
coef(fit2, matrix = TRUE)
c(Coef(fit2), colMeans(gdata))

# Essentially a 2-parameter gamma
gdata <- data.frame(y = rgamma(n = 500, rate = exp(-1), shape = exp(2)))
fit2 <- vglm(y ~ 1, gamma2, data = gdata, trace = TRUE, crit = "coef")
coef(fit2, matrix = TRUE)
c(Coef(fit2), colMeans(gdata))

VGAM documentation built on Sept. 19, 2023, 9:06 a.m.