Description Usage Arguments Details Value Examples

`MGRAF2`

returns the estimated common structure Z and *Λ* that
are shared by all the subjects as well as the subject-specific low rank
matrix *Q_i* for multiple undirected graphs.

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`A` |
Binary array with size VxVxn storing the VxV symmetric adjacency matrices of n graphs. |

`K` |
An integer that specifies the latent dimension of the graphs |

`tol` |
A numeric scalar that specifies the convergence threshold of CISE algorithm. CISE iteration continues until the absolute percent change in joint log-likelihood is smaller than this value. Default is tol = 0.01. |

`maxit` |
An integer that specifies the maximum number of iterations. Default is maxit = 5. |

The subject-specific deviation *D_i* is decomposed into

*D_i = Q_i
* Λ * Q_i^{\top},*

where each *Q_i* is a VxK orthonormal matrix and
*Λ* is a KxK diagonal matrix.

A list is returned containing the ingredients below from M-GRAF2 model corresponding to the largest log-likelihood over iterations.

`Z` |
A numeric vector containing the lower triangular entries in the estimated matrix Z. |

`Lambda` |
Kx1 vector storing the
diagonal entries in |

`Q` |
VxKxn array containing the estimated
VxK orthonormal matrix |

`D_LT` |
Lxn matrix where
each column stores the lower triangular entries in |

`LL_max` |
Maximum log-likelihood across iterations. |

`LL` |
Joint log-likelihood at each iteration. |

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