ggm.simulate.data {GeneTS} | R Documentation |
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).
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.uni-muenchen.de/~schaefer/) and Korbinian Strimmer (http://www.stat.uni-muenchen.de/~strimmer/).
Schaefer, J., and Strimmer, K. (2003). A practical approach to inferring large graphical models from sparse microarray data. Submitted to Bioinformatics [preprint available online].
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 <- 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)