# Combination of Factorial Methods and Cluster Analysis

### Description

Performs the factorial analysis of the data and a cluster analysis using the `nfcl`

first factorial
coordinates

### Usage

1 2 3 4 5 6 | ```
FactoClass( dfact, metodo, dfilu = NULL , nf = 2, nfcl = 10, k.clust = 3,
scanFC = TRUE , n.max = 5000 , n.clus = 1000 ,sign = 2.0,
conso=TRUE , n.indi = 25,row.w = rep(1, nrow(dfact)) )
## S3 method for class 'FactoClass'
print(x, ...)
analisis.clus(X,W)
``` |

### Arguments

`dfact ` |
object of class |

`metodo ` |
function of ade4 for |

`dfilu ` |
ilustrative variables (default NULL) |

`nf ` |
number of axes to use into the factorial analysis (default 2) |

`nfcl ` |
number of axes to use in the classification (default 10) |

`k.clust ` |
number of classes to work (default 3) |

`scanFC ` |
if is TRUE, it asks in the console the values |

`n.max ` |
when |

`n.clus ` |
when |

`sign ` |
threshold test value to show the characteristic variables and modalities |

`conso ` |
when |

`n.indi ` |
number of indices to draw in the histogram (default 25) |

`row.w ` |
vector containing the row weights if metodo<>dudi.coa |

`x ` |
object of class FactoClass |

`...` |
further arguments passed to or from other methods |

`X ` |
coordinates of the elements of a class |

`W ` |
weights of the elements of a class |

### Details

Lebart et al. (1995) present a strategy to analyze a data table using multivariate methods, consisting of an intial factorial analysis according to the nature of the compiled data, followed by the performance of mixed clustering. The mixed clustering combines hierarchic clustering using the Ward's method with K-means clustering. Finally a partition of the data set and the characterization of each one of the classes is obtained, according to the active and illustrative variables, being quantitative, qualitative or frequency.

FactoClass is a function that connects procedures of the package `ade4`

to perform the analysis
factorial of the data and from `stats`

for the cluster analysis.

The function `analisis.clus`

calculates the geometric characteristics of each class:
size, inertia, weight and square distance to the origin.

For impression in LaTeX format see FactoClass.tex

To draw factorial planes with cluster see plotFactoClass

### Value

object of class `FactoClass`

with the following:

`dudi ` |
object of class |

`nfcl ` |
number of axes selected for the classification |

`k ` |
number of classes |

`indices ` |
table of indices obtained through WARD method |

`cor.clus ` |
coordinates of the clusters |

`clus.summ ` |
summary of the clusters |

`cluster ` |
vector indicating the cluster of each element |

`carac.cate` |
cluster characterization by qualitative variables |

`carac.cont` |
cluster characterization by quantitative variables |

`carac.frec` |
cluster characterization by frequency active variables |

### Author(s)

Pedro Cesar del Campo pcdelcampon@unal.edu.co, Campo Elias Pardo cepardot@unal.edu.co http://www.docentes.unal.edu.co/cepardot, Ivan Diaz ildiazm@unal.edu.co, Mauricio Sadinle msadinleg@unal.edu.co

### References

Lebart, L. and Morineau, A. and Piron, M. (1995) Statisitique exploratoire multidimensionnelle, Paris.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
# Cluster analysis with Correspondence Analysis
data(ColorAdjective)
FC.col <-FactoClass(ColorAdjective, dudi.coa)
6
10
5
FC.col
FC.col$dudi
# Cluster analysis with Multiple Correspondence Analysis
data(BreedsDogs)
BD.act <- BreedsDogs[-7] # active variables
BD.ilu <- BreedsDogs[7] # ilustrative variables
FC.bd <-FactoClass( BD.act, dudi.acm, k.clust = 4,
scanFC = FALSE, dfilu = BD.ilu, nfcl = 10)
FC.bd
FC.bd$clus.summ
FC.bd$indices
``` |