cv.SBA: Cross validation for selecting optimal precision parameter in...

Description Usage Arguments Value References See Also Examples

View source: R/cv.SBA.R

Description

The performance of Stochastic Blockmodel Approximation (SBA) method is contingent on the number of blocks it finds during estimation process, which is rougly determined by a precision parameter delta. cv.SBA tests multiple of delta values to find the optimal one that minimizes the cross validation risk. Note that the optimal delta is not bound to be a single value.

Usage

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cv.SBA(A, vecdelta = seq(0.1, 1, by = 0.1))

Arguments

A

either

Case 1.

an (n\times n) binary adjacency matrix, or

Case 2.

a vector containing multiple of (n\times n) binary adjacency matrices.

vecdelta

a vector containing target delta values to be tested.

Value

a named list containing

optdelta

optimal delta values that minimize the cross validation risk J.

J

cross validation risk values.

References

\insertRef

chan2014graphon

\insertRef

Airoldi2013graphon

See Also

est.SBA

Examples

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## Not run: 
## generate a graphon of type No.8 with 3 clusters
W = gmodel.preset(3,id=8)

## create a probability matrix for 100 nodes
graphW = gmodel.block(W,n=100)
P = graphW$P

## draw 15 observations from a given probability matrix
A = gmodel.P(P,rep=15)

## cross validate SBA algorithm over different deltas
rescv = cv.SBA(A,vecdelta=c(0.1,0.5,0.9))
print(rescv$optdelta)

## End(Not run)

graphon documentation built on Aug. 13, 2021, 5:06 p.m.