# single.cut: Ratio cut and normalised cut values In FusedPCA: Community Detection via Fused Principal Component Analysis

## Description

Get the ratio cut and normalised cut values for a single community detection estimator.

## Usage

 `1` ```single.cut(A, clusters, K = 2) ```

## Arguments

 `A` input matrix – adjacency matrix of an observed graph based on the non-isolated nodes, of dimension `n.noniso` x `n.noniso`, where `n.noniso` is the number of the non-isolated nodes. `clusters` input vector – the estimator of the community labels of the non-isolated nodes in the network, of dimension `n.noniso`, values taken from 1 to K, where K is the number of communities. `K` the number of the communities, with 2 as the default value.

## Value

 `ratio.count` the value of the ratio cut. `normalised.count` the value of the normalised cut.

## Author(s)

Yang Feng, Richard J. Samworth and Yi Yu

## References

Yang Feng, Richard J. Samworth and Yi Yu, Community Detection via Fused Principal Component Analysis, manuscript.

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```## to generate an adjacency matrix A = matrix(c(0,1,1,1,0,0,1,0,0), byrow = TRUE, ncol = 3) ## have a look at A A ## ratio and normalised cut values ## given the community labels 1, 1 and 2 to nodes 1, 2 and 3 single.cut(A, c(1,1,2)) ```

### Example output

```Loading required package: genlasso

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

decompose, spectrum

The following object is masked from 'package:base':

union

[,1] [,2] [,3]
[1,]    0    1    1
[2,]    1    0    0
[3,]    1    0    0
\$ratio.count
[1] 1.5

\$normalised.count
[1] 1.333333
```

FusedPCA documentation built on May 29, 2017, 9:19 p.m.