| SimOneNorm.IG {SharedHT2} | R Documentation |
SimOneNorm.IG generates a single simulated micro-array expression
experiment under the Normal/Inverse Gamma model. This can be used
to generate a variety of example datasets.
SimOneNorm.IG(shape = NULL, rate = NULL, theta = NULL, ngroups, nreps, Ngenes,
effect.size)
shape |
The shape parameter for the Inverse Gamma distribution |
rate |
The rate parameter for the Inverse Gamma distribution |
theta |
Alternatively to specifying shape and rate
above, the user can directly specify the model parameters, i.e. the
logged shape and logged rate. |
ngroups |
The number of experimental groups. |
nreps |
Number of replicates per group. |
Ngenes |
Number of rows (or genes) in the dataset (micro-array experiment) |
effect.size |
A vector of length Ngenes giving the effect size.
Rows with population mean zero (not differentially expressed) are set to zero
while rows with non-zero population mean (differentially expressed) are set to
some non-zero value. For a feeling of corresponding power in the naive F test
of all means identically zero see the documetation on find.ncp by typing
?find.ncp. |
A dataframe having Ngenes rows and nreps * d columns where d
is implicit in the dimension of Lambda, (see above). See the documentation for
SimAffyDat for more details.
Grant Izmirlian izmirlian@nih.gov
EB.Anova, EBfit, SimAffyDat,
TopGenes, SimNorm.IG, SimMVN.IW,
SimMVN.mxIW, SimOneMVN.IW,
SimOneMVN.mxIW
## Not run:
shape <- 1.93589032
rate <- 0.04020591
Ngenes <- 12625
ngroups <- 2
nreps <- 3
nTP <- 100
effect.size <- c(rep(4.33, nTP), rep(0, Ngenes-nTP))
MyDat <- SimOneNorm.IG(shape=shape, rate=rate, ngroups=ngroups, Ngenes=Ngenes, nreps=nreps,
effect.size=effect.size)
# notice the names given to the columns by default:
names(MyDat)
# Now try out 'EB.Anova' on your dataset
fit.MyDat <- EB.Anova(data=MyDat, labels= "log2.grp" %,% (1:2), Var.Struct="simple",
H0="zero.means")
# View the sorted genelist
TopGenes(fit.MyDat, FDR=0.05, allsig=TRUE)
## End(Not run)