FactoClass {FactoClass} | R Documentation |
Performs the factorial analysis of the data and a cluster analysis using the nfcl
first factorial
coordinates
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 ) ## S3 method for class 'FactoClass': print(x, ...) analisis.clus(X,W)
dfact |
object of class data.frame , with the data of active variables. |
metodo |
function of ade4 for ade4 factorial analysis, dudi.pca ,Principal Component Analysis;
dudi.coa , Correspondence Analysis; witwit.coa , Internal Correspondence Analysis;
dudi.acm , Multiple Correspondence Analysis ... |
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 nf , nfcl y k.clust |
n.max |
when rowname(dfact)>=n.max , k-means is performed previous to hierarchical
clustering (default 5000) |
n.clus |
when rowname(fact)>=n.max , the previous k-means is performed with
n.clus groups (default 1000) |
sign |
threshold test value to show the characteristic variables and modalities |
conso |
when conso is TRUE, the process of consolidating the classification is
performed (default TRUE) |
n.indi |
number of indices to draw in the histogram (default 25) |
... |
|
x |
object of class FactoClass |
X |
coordinates of the elements of a class |
W |
weights of the elements of a class |
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
object of class FactoClass
with the following:
dudi |
object of class dudi from ade4 with the specifications of the factorial analysis |
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 |
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
Lebart, L. and Morineau, A. and Piron, M. (1995) Statisitique exploratoire multidimensionnelle, Paris.
# 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