\name{dcgamma} \alias{dcgamma} \alias{rcgamma} \alias{mcgamma} \title{ Approximate gamma shape distribution } \description{ \code{dcgamma} approximates density of a gamma shape distribution with a gamma density. \code{rcgamma} obtains random draws from the approximation. \code{mcgamma} computes approximated mean, variance and normalization constant. } \usage{ dcgamma(x, a, b, c, d, r, s, newton = TRUE) rcgamma(n, a, b, c, d, r, s, newton = TRUE) mcgamma(a, b, c, d, r, s, newton = TRUE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{ Vector indicating the values at which to evaluate the density. } \item{n}{ Number of random draws to obtain.} \item{a,b,c,d,r,s}{ Parameter values. } \item{newton}{ Set to \code{TRUE} to try to locate the mode by taking a few Newton-Raphson steps. } } \details{ The density of a gamma shape distribution is given by \code{C(a,b,c,d,r,s) (gamma(a*x+d)/gamma(x)^a) (x/(r+s*x))^{a*x+d} x^{b-d-1} exp(-x*c)} for \code{x>=0}, and 0 otherwise, where \code{C()} is the normalization constant. The gamma approximation is \code{Ga(a/2+b-1/2,c+a*log(s/a))}. The approximate normalization constant is obtained by taking the ratio of the exact density and the approximation at the maximum, as described in Rossell (2007). } \value{ \code{dcgamma} returns a vector with approximate density. \code{rcgamma} returns a vector with draws from the approximating gamma. \code{mcgamma} returns a list with components: \item{m }{Approximate mean} \item{v }{Approximate variance} \item{normk }{Approximate normalization constant} } \references{ Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. \url{http://rosselldavid.googlepages.com}. } \author{ David Rossell } \note{ For general values of the parameters the gamma approximation may be poor. In such a case one could use this function to obtain draws from the proposal distribution in a Metropolis-Hastings step. } \seealso{ \code{\link{dgamma}}, \code{\link{rgamma}} } \examples{ } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ distribution }