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.statistik.lmu.de/~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
.
## Not run: # 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 <- ggm.estimate.pcor(m.sim, method = c("observed.pcor")) # 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) ## End(Not run)