sim.gg {gaga} | R Documentation |
Simulates parameters and data from the prior-predictive of GaGa or MiGaGa model with 2 groups, fixing the hyper-parameters.
sim.gg(n, m, p.de=.1, a0, nu, balpha, nualpha, probclus=1, a=NA, l=NA, useal=FALSE)
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. |
The shape parameters are actually drawn from a gamma approximation to
their posterior distribution. The function rcgamma
implements
this approximation.
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. |
Currently, the routine only implements prior predictive simulation for the 2 hypothesis case.
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
simnewsamples
to simulate from the posterior
predictive, checkfit
for graphical posterior predictive checks.
#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')