simGG {gaga} | R Documentation |
Simulates parameters and data from the prior-predictive of GaGa or MiGaGa model with several groups, fixing the hyper-parameters.
simGG(n, m, p.de=.1, a0, nu, balpha, nualpha, equalcv = TRUE, 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) . |
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. |
The shape parameters are actually drawn from a gamma approximation to
their posterior distribution. The function rcgamma
implements
this approximation.
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)
.
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 <- 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')