cv.SBA {graphon} | R Documentation |
Cross validation for selecting optimal precision parameter in SBA method.
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
cv.SBA(A, vecdelta = seq(0.1, 1, by = 0.1))
Arguments
A |
either
|
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
Chan SH, Airoldi EM (2014). “A Consistent Histogram Estimator for Exchangeable Graph Models.” In Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32, ICML'14, I-208–I-216.
Airoldi EM, Costa TB, Chan SH (2013). “Stochastic blockmodel approximation of a graphon: Theory and consistent estimation.” In Burges CJC, Bottou L, Welling M, Ghahramani Z, Weinberger KQ (eds.), Advances in Neural Information Processing Systems 26, 692–700. Curran Associates, Inc.
See Also
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)