Description Usage Arguments Value References See Also Examples

`est.LG`

takes a 2-stage approach. First it adopts largest gap criterion on empirical degrees to
estimate blocks of a given network under Stochastic Blockmodel framework.
Then a consistent histogram estimator is utilized to estimate graphons based on
estimated blocks in a given network.

1 | ```
est.LG(A, K)
``` |

`A` |
an |

`K` |
the number of blocks provided by an user. |

a named list containing

- H
a

*(K\times K)*matrix of 3D histogram.- P
an

*(n\times n)*corresponding probability matrix.- B
a length-

*K*list where each element is a vector of nodes/indices for each cluster.

Channarond2011graphon

\insertRefchan2014graphon

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
## Not run:
## generate a graphon of type No.10 with 3 clusters
W = gmodel.preset(3,id=10)
## create a probability matrix for 100 nodes
graphW = gmodel.block(W,n=100)
P = graphW$P
## draw 23 observations from a given probability matrix
A = gmodel.P(P,rep=23)
## run LG algorithm with a rough guess for K=2,3,4
res2 = est.LG(A,K=2)
res3 = est.LG(A,K=3)
res4 = est.LG(A,K=4)
## compare true probability matrix and estimated ones
par(mfrow=c(1,4))
image(P); title("main")
image(res2$P); title("LG with K=2")
image(res3$P); title("LG with K=3")
image(res4$P); title("LG with K=4")
## End(Not run)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.