OptiNum {CluMP} | R Documentation |

## Finding an optimal number of clusters

### Description

This function finds optimal number of clusters based on evaluation criteria (indices) available from the NbClust package.

### Usage

```
OptiNum(
formula,
group,
data,
index = c("silhouette", "ch", "db"),
max_clust = 10,
base_val = FALSE
)
```

### Arguments

`formula` |
A two-sided |

`group` |
A grouping factor variable (vector), i.e. single identifier for each individual (trajectory). |

`data` |
A data frame containing the variables named in |

`index` |
String vector of indices to be computed. Default is c("silhouette", "ch", "db"). See NbClust package for available indices and their description. |

`max_clust` |
An integer, positive number (scalar) defining the maximum number of clusters to check. Default value of this argument is 10 or maximum number of individuals. |

`base_val` |
Indicates whether include a value at zero time point as an additional clustering variable. Default is |

### Value

Determine the optimal number of clusters, returns graphical output (red dot in plot indicates the recommended number of clusters according to that index) and table with indices.

### Source

Malika Charrad, Nadia Ghazzali, Veronique Boiteau, Azam Niknafs (2014). NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. Journal of Statistical Software, 61(6), 1-36. URL http://www.jstatsoft.org/v61/i06/.

### Examples

```
set.seed(123)
data <- GeneratePanel(n = 100, Param = ParamLinear, NbVisit = 10)
OptiNum(data = data, formula = Y ~ Time, group = "ID")
```

*CluMP*version 0.8.1 Index]