`pcorSimulator`

creates a block diagonal positive definite precision matrix with three possible
graph structures: hubs-based, power-law and random. Then, it generates samples from a multivariate normal
distribution with covariance matrix given by the inverse of such precision matrix.

1 2 3 4 5 | ```
pcorSimulator(nobs, nclusters, nnodesxcluster, pattern = "powerLaw",
low.strength = 0.5, sup.strength = 0.9, nhubs = 5,
degree.hubs = 20, nOtherEdges = 30, alpha = 2.3, plus = 0,
prob = 0.05, perturb.clust = 0, mu = 0,
probSign = 0.5, seed = sample(10000, nclusters))
``` |

`nobs` |
number of observations. |

`nclusters` |
number of clusters or blocks of variables. |

`nnodesxcluster` |
number of nodes/variables per cluster. |

`pattern` |
graph structure pattern: name that uniquely identifies |

`low.strength` |
minimum magnitude for nonzero partial correlation elements before regularization. |

`sup.strength` |
maximum magnitude for nonzero partial correlation elements before regularization. |

`nhubs` |
number of hubs per cluster (if |

`degree.hubs` |
degree of hubs (if |

`nOtherEdges` |
number of edges for non-hub nodes (if |

`alpha` |
positive coefficient for the Riemman function in power-law distributions. |

`plus` |
power-law distribution added complexity (zero by default). |

`prob` |
probability of edge presence for random networks (if |

`perturb.clust` |
proportion of the total number of edges that are connecting two different clusters. |

`mu` |
expected values vector to generate data (zero by default). |

`probSign` |
probability of positive sign for non-zero partial correlation coefficients. Thus, negative signs
are obtained with probability |

`seed` |
vector with seeds for each cluster. |

Hubs-based networks are graphs where only few nodes have a much higher degree
(or connectivity) than the rest. Power-law networks assume that the variable *p_k*,
which denotes the fraction of nodes in the network that has degree *k*,
is given by a power-law distribution

*
p_k = \frac{k^{-α}}{\varsigma(α)},
*

for *k ≥q 1*, a constant *α>0* and the normalizing function *\varsigma(α)*
which is the Riemann zeta function. Finally, random networks are also defined by the distribution in
the proportion *p_k*. In this case, *p_k* follows a binomial distribution

*
p_k = {p\choose k} θ^k (1-θ)^{p-k},
*

where the parameter *θ* determines the proportion of edges (or sparsity) in the graph.

The regularization is given by *Ω^{(1)} = Ω^{(0)} + δ I*, with *δ*
such that the condition number of *Ω^{(1)}* is less than the number of nodes.

An object of class `pcorSim`

containing the following components:

`y ` |
generated data set. |

`hubs ` |
hub nodes position. |

`edgesInGraph ` |
edges given by the non-zero elements in the precision matrix. |

`omega ` |
precision matrix used to generate the data. |

`covMat ` |
covariance matrix used to generate the data. |

`path ` |
adjacency matrix corresponding to the non-zero structure of |

Caballe, Adria <a.caballe@sms.ed.ac.uk>, Natalia Bochkina and Claus Mayer.

Cai, T., W. Liu, and X. Luo (2011). A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation. Journal of the American Statistical Association 106(494), 594-607.

Newman, M. (2003). The structure and function of complex networks. SIAM REVIEW 45, 167-256.

Caballe, A., N. Bochkina, and C. Mayer (2016). Selection of the Regularization Parameter in Graphical Models using network charactaristics. eprint arXiv:1509.05326, 1-25.

`plot.pcorSim`

for graphical representation of the generated partial correlation matrix.

`pcorSimulatorJoint`

for joint partial correlation matrix generation.

1 2 3 4 5 6 7 8 9 10 | ```
# example to use pcorSimulator function
EX1 <- pcorSimulator(nobs = 50, nclusters=3, nnodesxcluster=c(100,30,50),
pattern="powerLaw", plus=0)
print(EX1)
EX2 <- pcorSimulator(nobs = 25, nclusters=2, nnodesxcluster=c(60,40),
pattern="powerLaw", plus=1)
print(EX2)
``` |

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