network.mixing.Bfold | R Documentation |

estimates network connection probability by network mixing of Li and Le (2021) with B-fold averaging.

```
network.mixing.Bfold(A,B=10,rho = 0.1,max.K=15,dcsbm=TRUE,usvt=TRUE,ns=FALSE,
lsm=FALSE,lsm.k=4)
```

`A` |
adjacency matrix |

`B` |
number of random replications to average over |

`rho` |
hold-out proportion as validation entries. Only effective when index is NULL. |

`max.K` |
the maximum number of blocks used for the block model approximation (see details). |

`dcsbm` |
whether to include the DCSBM as components, up to max.K. By default, the method will include it. |

`usvt` |
whether to include the USVT as a component. By default, the method will include it. |

`ns` |
whether to include the neighborhood smoothing as a component. |

`lsm` |
whether to include the gradient estimator of the latent space model as a component. |

`lsm.k` |
the dimension of the latent space. Only effective if lsm is TRUE. |

This is essentially the same procedure as the network.mixing, but repeat it B times and return the average. Use with cautious. Though it can make the estimate more stable, the procedure would increase the computational cost by a factor of B. Based on our limited experience, this is usually not a great trade-off as the improvement might be marginal.

a list of

`linear.Phat ` |
estimated probability matrix by linear mixing |

`nnl.Phat ` |
estimated probability matrix by NNL mixing |

`exp.Phat ` |
estimated probability matrix by exponential mixing |

`ecv.Phat ` |
estimated probability matrix by ECV mixing (only one nonzero) |

`model.names` |
the names of all individual models, in the same order as the weights |

Tianxi Li and Can M. Le

Maintainer: Tianxi Li <tianxili@virginia.edu>

T. Li and C. M. Le, Network Estimation by Mixing: Adaptivity and More. arXiv preprint arXiv:2106.02803, 2021.

`network.mixing`

```
dt <- RDPG.Gen(n=200,K=3,directed=TRUE)
A <- dt$A
fit <- network.mixing.Bfold(A,B=2)
```

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