SimOneMVN.IW {SharedHT2}R Documentation

Simulate a micro-array experiment dataset

Description

SimOneMVN.IW generates a single simulated micro-array expression experiment under the Multivariate Normal/Inverse Wishart model. This can be used to generate a variety of example datasets.

Usage

SimOneMVN.IW(shape = NULL, rate = NULL, theta = NULL, nreps, Ngenes, 
           effect.size) 

Arguments

shape The shape parameter for the Inverse Wishart distribution
rate The rate parameter matrix, of dimension d by d where d is the number of experimental groups.
theta Alternatively to specifying shape and rate above, the user can directly specify the model parameters from which shape and rate are computed. Type ?EBfit for more details.
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.

Value

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.

Author(s)

Grant Izmirlian izmirlian@nih.gov

See Also

EB.Anova, EBfit, SimAffyDat, TopGenes, SimNorm.IG, SimMVN.IW, SimMVN.mxIW, SimOneNorm.IG, SimOneMVN.mxIW

Examples

## Not run: 
  shape <- 9.107182
  rate <- matrix(c(0.12789434, 0.08468535, 0.08468535, 0.12390469), 2, 2)
  Ngenes <- 12625
  nreps <- 3
  nTP <- 100
  effect.size <- c(rep(4.33, nTP), rep(0, Ngenes-nTP))

  MyDat <- SimOneMVN.IW(shape=shape, rate=rate, 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), H0="zero.means")

# View the sorted genelist

  TopGenes(fit.MyDat, FDR=0.05, allsig=TRUE)
## End(Not run)

[Package SharedHT2 version 2.0 Index]