sim.gg {gaga}R Documentation

Prior predictive simulation

Description

Simulates parameters and data from the prior-predictive of GaGa or MiGaGa model with 2 groups, fixing the hyper-parameters.

Usage

sim.gg(n, m, p.de=.1, a0, nu, balpha, nualpha, probclus=1, a=NA, l=NA, useal=FALSE)

Arguments

n Number of genes.
m Vector indicating number of observations to be simulated for each group.
p.de Probability that a gene is differentially expressed.
a0, nu Mean expression for each gene is generated from 1/rgamma(a0,a0/nu) if probclus is of length 1, and from a mixture if length(probclus)>1.
balpha, nualpha Shape parameter for each gene is generated from rgamma(balpha,balpha/nualpha).
probclus Vector with the probability of each component in the mixture. Set to 1 for the GaGa model.
a, l Optionally, if useal==TRUE the parameter values are not generated, only the data is generated. a is a matrix with the shape parameters of each gene and group and l is a matrix with the mean expressions.
useal For useal==TRUE the parameter values specified in a and l are used, instead of being generated.

Details

The shape parameters are actually drawn from a gamma approximation to their posterior distribution. The function rcgamma implements this approximation.

Value

A list with the following components:

x Matrix with simulated observations (group 1 in columns 1:m[1], group 2 in columns (m[1]+1):(m[1]+m[2]) etc.)
a Matrix with shape parameters used to generate x.
l Matrix with mean expression used to generate x.

Note

Currently, the routine only implements prior predictive simulation for the 2 hypothesis case.

Author(s)

David Rossell

References

Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.

See Also

simnewsamples to simulate from the posterior predictive, checkfit for graphical posterior predictive checks.

Examples

#Not run. Example from the help manual
#library(gaga)
#set.seed(10)
#n <- 100; m <- c(6,6)
#a0 <- 25.5; nu <- 0.109
#balpha <- 1.183; nualpha <- 1683
#probpat <- c(.95,.05)
#xsim <- sim.gg(n,m,p.de=probpat[2],a0,nu,balpha,nualpha)
#
#plot(density(xsim$x),main='')
#plot(xsim$l,xsim$a,ylab='Shape',xlab='Mean')

[Package gaga version 1.0.0 Index]