FactoClass (version 1.1.6)

cluster.carac: Cluster Characterization by Variables

Description

It makes the characterization of the classes or cluster considering the variables in tabla. These variables can be quantitative, qualitative or frequencies.

Usage

cluster.carac( tabla,class,tipo.v="d",v.lim= 2,dn=3,dm=3,neg=TRUE)

Arguments

tabla

object data.frame with variables of characterization, the variables must be of a single type (quantitative, qualitative or frequencies)

class

vector that determines the partition of the table

tipo.v

type of variables: quantitative("continuas"), qualitative ("nominales") or frequencies("frecuencia")

v.lim

test value to show the variable or category like characteristic.

dn

number of decimal digits for the p and test values.

dm

number of decimal digits for the means.

neg

if neg=TRUE, the variables or categories with negative test values are showed.

Value

Object of class list. It has the characterization of each class or cluster.

Details

For nominal or frecuency variables it compares the percentage of the categories within each class with the global percentage. For continuous variables it compares the average within each class with the general average. Categories and variables are ordered within each class by the test values and it shows only those that pass the threshold v.lim.

References

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

Examples

# NOT RUN {
data(BreedsDogs)
BD.act <- BreedsDogs[-7]  # active variables
BD.function <- subset(BreedsDogs,select=7)   
cluster.carac(BD.act,BD.function,"ca",2.0)  #  nominal variables


data(iris)
iris.act <- Fac.Num(iris)$numeric
class <- Fac.Num(iris)$factor
cluster.carac(iris.act,class,"co",2.0)  #  continuous variables

# frequency variables
data(BreedsDogs)
attach(BreedsDogs)
weig<-table(FUNC,WEIG)
weig<-data.frame(weig[,1],weig[,2],weig[,3])
cluster.carac(weig,  row.names(weig), "fr", 2) # frequency variables
detach(BreedsDogs)
# }