ggm.simulate.data {GeneTS}R Documentation

Graphical Gaussian Models: Simulation of of Data

Description

ggm.simulate.data takes a positive definite partial correlation matrix and generates an iid sample from the corresponding standard multinormal distribution (with mean 0 and variance 1).

Usage

ggm.simulate.data(sample.size, pcor)

Arguments

sample.size sample size of simulated data set
pcor partial correlation matrix

Value

A multinormal data matrix.

Author(s)

Juliane Schaefer (http://www.stat.uni-muenchen.de/~schaefer/) and Korbinian Strimmer (http://www.stat.uni-muenchen.de/~strimmer/).

References

Schaefer, J., and Strimmer, K. (2004). An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics in press.

See Also

ggm.simulate.pcor, ggm.estimate.pcor.

Examples

# load GeneTS library
library(GeneTS)

# generate random network with 40 nodes 
# it contains 780=40*39/2 edges of which 5 percent (=39) are non-zero
true.pcor <- ggm.simulate.pcor(40)
  
# simulate data set with 40 observations
m.sim <- ggm.simulate.data(40, true.pcor)

# simple estimate of partial correlations
estimated.pcor <- partial.cor(m.sim)

# comparison of estimated and true model
sum((true.pcor-estimated.pcor)^2)

# a slightly better estimate ...
estimated.pcor.2 <- ggm.estimate.pcor(m.sim, method = c("bagged.pcor"))
sum((true.pcor-estimated.pcor.2)^2)

[Package GeneTS version 2.3 Index]