Get the ratio cut and normalised cut values for the estimators along the path. It is part of the function in fpca
, the main part of this function is single.cut
.
1  fpca.cut(A, obj, fpca.cluster, K = 2, iso.seq)

A 
input matrix – adjacency matrix of an observed graph based on the nonisolated nodes, of dimension 
obj 
a 
fpca.cluster 
a list of vectors, with each vector as the estimator of the community labels of the nonisolated nodes in the network, of dimension 
K 
the number of the communities, with 2 as the default value. 
iso.seq 
a vector of the indices of those isolated nodes in the graph. If it is missing, 
ratio.list 
a list of ratio cut values for the estimator path. 
normalised.list 
a list of normalised cut values for the estimator path. 
Yang Feng, Richard J. Samworth and Yi Yu
Yang Feng, Richard J. Samworth and Yi Yu, Community Detection via Fused Principal Component Analysis, manuscript. Holland, P.W., Laskey, K.B. and Leinhardt, S., 1983. Stochastic block models: first steps. Social Networks 5, 109137. Jin, J., 2012. Fast community detection by score. Karrer, B. and Newman, M.E.J., 2011. Stochastic blockmodels and community structure in networks. Physical Review E 83, 016107.
single.cut
, fpca
, fpca.start
.
1  ### please see the examples in fpca

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