Description Usage Arguments Details Value Author(s) References Examples

Estimate multiple, related sparse Gaussian or Nonparanormal graphical models from multiple related datasets using the SIMULE algorithm. Please run demo(wsimule) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) doi10.1007/s10994-017-5635-7.

1 2 | ```
wsimule(X, lambda, epsilon = 1, W, covType = "cov",
intertwined = FALSE, parallel = FALSE)
``` |

`X` |
A List of input matrices. They can be data matrices or covariance/correlation matrices. If every matrix in the X is a symmetric matrix, the matrices are assumed to be covariance/correlation matrices. More details at https://github.com/QData/SIMULE |

`lambda` |
A positive number. The hyperparameter controls the sparsity
level of the matrices. The |

`epsilon` |
A positive number. The hyperparameter controls the
differences between the shared pattern among graphs and the individual part
of each graph. The |

`W` |
A weight matrix. This matrix uses the prior knowledge of the
graphs. For example, if we use wsimule to infer multiple human brain
connectome graphs, the |

`covType` |
A parameter to decide which Graphical model we choose to estimate from the input data. If covType = "cov", it means that we estimate multiple sparse Gaussian Graphical models. This option assumes that we calculate (when input X represents data directly) or use (when X elements are symmetric representing covariance matrices) the sample covariance matrices as input to the simule algorithm. If covType = "kendall", it means that we estimate multiple nonparanormal Graphical models. This option assumes that we calculate (when input X represents data directly) or use (when X elements are symmetric representing correlation matrices) the kendall's tau correlation matrices as input to the simule algorithm. |

`intertwined` |
indicate whether to use intertwined covariance matrix |

`parallel` |
A boolean. This parameter decides if the package will use the multithreading architecture or not. |

The SIMULE algorithm is a constrained l1 minimization method that can detect both the shared and the task-specific parts of multiple graphs explicitly from data (through jointly estimating multiple sparse Gaussian graphical models or Nonparanormal graphical models). It solves the following equation:

* \hat{Ω}^{(1)}_I, \hat{Ω}^{(2)}_I, …,
\hat{Ω}^{(K)}_I, \hat{Ω}_S =
\min\limits_{Ω^{(i)}_I,Ω_S}∑\limits_i ||W \cdot
Ω^{(i)}_I||_1+ ε K||W \cdot Ω_S||_1 *

Subject to :

*
||Σ^{(i)}(Ω^{(i)}_I + Ω_S) - I||_{∞} ≤ λ_{n}, i
= 1,…,K \nonumber *

Please also see the equation (7) in our paper. The
*λ_n* is the hyperparameter controlling the sparsity level of the
matrices and it is the `lambda`

in our function. The *ε* is
the hyperparameter controlling the differences between the shared pattern
among graphs and the individual part of each graph. It is the `epsilon`

parameter in our function and the default value is 1. For further details,
please see our paper:
http://link.springer.com/article/10.1007/s10994-017-5635-7.

if labels are provided in the datalist as column names, result will contain labels (to be plotted)

`$graphs` |
A list of the estimated inverse covariance/correlation matrices. |

`$share` |
The share graph among multiple tasks. |

Beilun Wang

Beilun Wang, Ritambhara Singh, Yanjun Qi (2017). A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models. http://link.springer.com/article/10.1007/s10994-017-5635-7

1 2 3 4 5 | ```
library(JointNets)
data(exampleData)
result = wsimule(X = exampleData , lambda = 0.1, epsilon = 0.45,
W = matrix(1,20,20), covType = "cov", FALSE)
plot(result)
``` |

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