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)