cv.SBA function

Cross validation for selecting optimal precision parameter in SBA method.

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.

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.

Returns

a named list containing

• optdelta: optimal delta values that minimize the cross validation risk J.
• J: cross validation risk values.

Examples

## 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)

References

Rdpack::insert_ref(key="chan2014",package="graphon")

Rdpack::insert_ref(key="Airoldi2013",package="graphon")

See Also

est.SBA