| ggm.simulate.data {GeneTS} | R Documentation |
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.
ggm.simulate.data(sample.size, pcor)
sample.size |
sample size of simulated data set |
pcor |
partial correlation matrix |
A multinormal data matrix.
Juliane Schaefer (http://www.stat.math.ethz.ch/~schaefer/) and Korbinian Strimmer (http://www.statistik.lmu.de/~strimmer/).
Schaefer, J., and Strimmer, K. (2005). An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21:754-764.
ggm.simulate.pcor, ggm.estimate.pcor.
# 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)