network.mixing.Bfold: estimates network connection probability by network mixing...

In randnet: Random Network Model Estimation, Selection and Parameter Tuning

estimates network connection probability by network mixing with B-fold averaging

Description

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

Usage

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

Arguments

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.

Details

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.

Value

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

Author(s)

Tianxi Li and Can M. Le

Maintainer: Tianxi Li <[e-mail]>

References

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

See Also

network.mixing

Examples

dt <- RDPG.Gen(n=200,K=3,directed=TRUE)
A <- dt$A

fit <- network.mixing.Bfold(A,B=2)

randnet documentation built on May 31, 2023, 6:44 p.m.