simGG {gaga}R Documentation

Prior predictive simulation

Description

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

Usage

simGG(n, m, p.de=.1, a0, nu, balpha, nualpha, equalcv = TRUE, 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).
equalcv If equalcv==TRUE the shape parameter is simulated to be constant across groups.
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

Object of class 'ExpressionSet'. Expression values can be accessed via exprs(object) and the parameter values used to generate the expression values can be accessed via fData(object).

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 <- simGG(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 1.2.0 Index]