gen.Network {MNS}

Simulate random networks for a population of subjects

Description

Implementations of two methods through which to simulation multiple related networks. The first simulates networks from a three-class population described in Danaher et al. (2014). The second simulates networks according to method proposed in Monti et al. (2015). For further details see the package vignette.

Usage

gen.Network(method = "cohort", p,
    Nobs, Nsub, sparsity,
    REsize, REprob, REnoise)

Arguments

method

Network simulation method. One of either "Danaher" for the three-class method of Danaher et al. (2014) or "cohort" for the cohort method described in Monti et al. (2015)

p

Number of nodes in network (i.e., this will be dimensionality of the resulting precision matrices)

Nobs

Number of observations per subject (assumed constant across subjects). If this is missing then only the precision matrices will be returned (i.e., random data is not simulated)

Nsub

Number of subjects for which to simulate networks. Note that this is set to 3 if method="Danaher"

sparsity

Sparsity level of precision matrices

REsize

Number of random effects edges to add to each subject (only for method="cohort")

REprob

Probability with which a random edge added to each subject (only for method="cohort")

REnoise

Variability of random edges (only for method="cohort")

Details

See package vignette for further details. Alternatively see Danaher et al. (2014) or Monti et al. (2015)

Value

Networks

List containing simulated netowrks where ith entry is the ith random network for the ith subject

Data

List where ith entry is simulated data for ith subject

PopNet

Population precision matrix (only if method="cohort")

RanNet

Sparse support for random edges (only if method="cohort")

Author(s)

Ricardo Pio Monti

References

Danaher, P., Wang, P. , and Witten, D. "The joint graphical lasso for inverse covariance estimation across multiple classes." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76.2 (2014): 373-397.

Monti, R., Anagnostopolus, C., Montana, G. "Inferring brain connectivity networks from functional MRI data via mixed neighbourhood selection", arXiv, 2015

See Also

MNS, cv.MNS, plot.MNS

Examples

# generate data according to cohort model of Monti et al. (2015)
set.seed(1)
Dat = gen.Network(p = 10, Nsub = 5, 
 sparsity = .2, REsize=10, REprob=.5, 
 REnoise = 1, Nobs=20)

## Not run: 
# plot simulated networks:
plot(Net, view="pop")

## End(Not run)