Description Usage Arguments Value Warning Author(s) References See Also Examples
To use k-means to cluster the estimators along the path and get the community labels. fpca.cluster
is a wrap up of functions fpca.nonscore.cluster
and fpca.score.cluster
. The latter two are the clustering functions for the situations in which score = FALSE
and score = TRUE
, respectively.
1 2 3 | fpca.cluster(obj, K = 2, score = F)
fpca.nonscore.cluster(obj, K = 2)
fpca.score.cluster(obj, K = 2)
|
obj |
in function In functions |
K |
input integer â the pre-specified number of communities, with the default value 2. |
score |
indicator argument â whether to apply the score associated clustering method or not, with the default value FALSE. |
an array of community labels list, of dimension number of non-isolated nodes x number of effective estimators. Each entry has value from 1 to K, as an index of the community label. Notice, the community labels are usually permutation-invariant.
if the input object obj
is a FPCA
object, the supposed value for score
should be F
. If users set score = T
, the function will stop with warning 'This object is designed for 'score = F''
. If the input object obj
is a FPCA-RoE
object, the supposed value for score
should be T
. If users set score = F
, the function will still execute, but with warning 'This object is designed for 'score = Tâ.
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, 109-137. 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.
fpca.nonscore.cluster
, fpca.score.cluster
, fpca.start
, fpca.nonscore
, fpca.score
.
1 | ### please see the examples in fpca
|
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