Description Usage Arguments Details Value References See Also Examples

This function implements clustering and dimension reduction for mixed-type variables, i.e., categorical and metric (see, Yamamoto & Hwang, 2014; van de Velden, Iodice D'Enza, & Markos 2019; Vichi, Vicari, & Kiers, 2019). This framework includes Mixed Reduced K-means and Mixed Factorial K-means, as well as a compromise of these two methods. The methods combine Principal Component Analysis of mixed-data for dimension reduction with K-means for clustering.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
cluspcamix(data, nclus, ndim, method=c("mixedRKM", "mixedFKM"),
center = TRUE, scale = TRUE, alpha=NULL, rotation="none",
nstart = 100, smartStart=NULL, seed=NULL, binary = FALSE)
## S3 method for class 'cluspcamix'
print(x, ...)
## S3 method for class 'cluspcamix'
summary(object, ...)
## S3 method for class 'cluspcamix'
fitted(object, mth = c("centers", "classes"), ...)
``` |

`data` |
Dataset with categorical and metric variables |

`nclus` |
Number of clusters (nclus = 1 returns the PCAMIX solution) |

`ndim` |
Dimensionality of the solution |

`method` |
Specifies the method. Options are mixedRKM for mixed reduced K-means and mixedFKM for mixed factorial K-means (default = |

`center` |
A logical value indicating whether the variables should be shifted to be zero centered (default = |

`scale` |
A logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place (default = |

`alpha` |
Adjusts for the relative importance of Mixed RKM and Mixed FKM in the objective function; |

`rotation` |
Specifies the method used to rotate the factors. Options are |

`nstart` |
Number of random starts (default = 100) |

`smartStart` |
If |

`seed` |
An integer that is used as argument by |

`binary` |
If |

`x` |
For the |

`object` |
For the |

`mth` |
For the |

`...` |
Not used |

For the K-means part, the algorithm of Hartigan-Wong is used by default.

The hidden `print`

and `summary`

methods print out some key components of an object of class `cluspcamix`

.

The hidden `fitted`

method returns cluster fitted values. If method is `"classes"`

, this is a vector of cluster membership (the cluster component of the "cluspcamix" object). If method is `"centers"`

, this is a matrix where each row is the cluster center for the observation. The rownames of the matrix are the cluster membership values.

When `nclus`

= 1 the function returns the solution of PCAMIX and `plot(object)`

shows the corresponding biplot.

`obscoord` |
Object scores |

`attcoord` |
Variable scores |

`centroid` |
Cluster centroids |

`cluster` |
Cluster membership |

`criterion` |
Optimal value of the objective criterion |

`size` |
The number of objects in each cluster |

`scale` |
A copy of |

`center` |
A copy of |

`nstart` |
A copy of |

`odata` |
A copy of |

van de Velden, M., Iodice D'Enza, A., & Markos, A. (2019).
Distance-based clustering of mixed data. *Wiley Interdisciplinary Reviews: Computational Statistics*, e1456.

Vichi, M., Vicari, D., & Kiers, H.A.L. (2019). Clustering and
dimension reduction for mixed variables. *Behaviormetrika*. doi:10.1007/s41237-018-0068-6.

Yamamoto, M., & Hwang, H. (2014). A general formulation of
cluster analysis with dimension reduction and subspace
separation. *Behaviormetrika*, *41*, 115-129.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
data(diamond)
#Mixed Reduced K-means solution with 3 clusters in 2 dimensions
#after 10 random starts
outmixedRKM = cluspcamix(diamond, 3, 2, method = "mixedRKM", nstart = 10, seed = 1234)
outmixedRKM
#Scatterplot (dimensions 1 and 2)
plot(outmixedRKM)
#Tandem analysis: PCAMIX followed by K-means solution
#with 3 clusters in 2 dimensions after 10 random starts
outTandem = cluspcamix(diamond, 3, 2, alpha = 1, nstart = 10, seed = 1234)
outTandem
#Scatterplot (dimensions 1 and 2)
plot(outTandem)
#nclus = 1 just gives the PCAMIX solution
#outPCAMIX = cluspcamix(diamond, 1, 2)
#outPCAMIX
#Biplot (dimensions 1 and 2)
#plot(outPCAMIX)
``` |

clustrd documentation built on May 8, 2019, 5:03 p.m.

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