ggm.simulate.data {GeneTS}R Documentation

Graphical Gaussian Models: Simulation of Data

Description

ggm.simulate.data takes a positive definite partial correlation matrix and generates an i.i.d. sample from the corresponding standard multinormal distribution (with mean 0 and variance 1). If the input matrix pcor is not positive definite an error is thrown.

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.math.ethz.ch/~schaefer/) and Korbinian Strimmer (http://www.statistik.lmu.de/~strimmer/).

References

Schaefer, J., and Strimmer, K. (2005). An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21:754-764.

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 <- cor2pcor( cor(m.sim) )

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

# a slightly better estimate ...
estimated.pcor.2 <- ggm.estimate.pcor(m.sim)
sum((true.pcor-estimated.pcor.2)^2)


[Package GeneTS version 2.10.1 Index]