ggm.simulate.data {GeneNet}

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 Sch\"afer and Korbinian Strimmer (https://strimmerlab.github.io).

References

Sch\"afer, 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 GeneNet library
library("GeneNet")

# 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)