View source: R/CC_with_robScore_functions.R

consensus_matrix | R Documentation |

Calculate consensus matrix for data perturbation consensus clustering

```
consensus_matrix(
X,
max.cluster = 5,
resample.ratio = 0.7,
max.itter = 100,
clustering.method = "hclust",
adj.conv = TRUE,
verbos = TRUE
)
```

`X` |
adjacency matrix a Nsample x Nsample |

`max.cluster` |
maximum number of clusters |

`resample.ratio` |
the data ratio to use at each itteration. |

`max.itter` |
maximum number of itterations at each |

`clustering.method` |
base clustering method: |

`adj.conv` |
binary value to apply soft thresholding (default= |

`verbos` |
binary value for verbosity (default= |

performs data perturbation consensus clustering and obtain consensus matrix Monti et al. (2003) consensus clustering algorithm

list of consensus matrices for each k

```
X = gaussian_clusters()$X
Adj = adj_mat(X, method = "euclidian")
CM = consensus_matrix(Adj, max.cluster=3, max.itter=10, verbos = FALSE)
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.