Description Usage Arguments Value References See Also Examples
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.
1 |
A |
either
|
vecdelta |
a vector containing target delta values to be tested. |
a named list containing
optimal delta values that minimize the cross validation risk J.
cross validation risk values.
chan2014graphon
\insertRefAiroldi2013graphon
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.