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 non-isolated nodes, of dimension |

`obj` |
a |

`fpca.cluster` |
a list of vectors, with each vector as the estimator of the community labels of the non-isolated 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, 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.

1 | ```
### please see the examples in fpca
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.