Localities by Stratums in Bogota City

Description

Contingency Table that indicates the number of blocks of Bogota, in localities by stratums (DAPD 1997, p.77).

Usage

data(Bogota)

Format

Object whit class data.frame of 19 rows and 7 columns.

Source

DAPD (1997), Population, stratification and socioeconomic aspects of Bogota

References

C.E. Pardo y J.E. Ortiz (2004). Analisis multivariado de datos en R. Simposio de Estadistica, Cartagena Colombia.

Associations between colors and adjectives.

Description

A group of students from Nanterre University (Paris X) were presented with a list of eleve colours: blue, yellow, red, white, pink, brown, purple, grey, black, green and orange. Each person in the group was asked to describe each color with one ore more adjectives. A final list of 89 adjectives were associates with eleven colors.

Usage

data(ColorAdjective)

Format

Object of class data.frame with 89 rows and 11 columns.

Source

Jambu, M. and Lebeaux M.O. Cluster Analysis and Data Analysis. North-Holland. Amsterdam 1983.

References

Fine, J. (1996), Iniciacion a los analisis de datos multidimensionales a partir de ejemplos, Notas de curso, Montevideo

Dog Breeds

Description

Table that describes 27 dog breeds considering their size, weight, speed, intelligence, affectivity, aggressiveness and function.

Usage

data(DogBreeds)

Format

Object of class data.frame with 27 rows and 7 columns with the following description:

Source

Fine, J. (1996), 'Iniciacion a los analisis de datos multidimensionales a partir de ejemplos', Notas de clase, Montevideo.

References

Brefort, A.(1982), 'Letude des races canines a partir de leurs caracteristiques qualitatives', HEC - Jouy en Josas

Division of qualitative and quantitative variables

Description

An object of class data.frame is divided into a list with two tables, one with quantitative variables and the other with qualitative variables.

Usage

Fac.Num(tabla)

Arguments

Value

It returns one list with one or two objects of class data.frame with the following characteristics:

Author(s)

Pedro Cesar Del Campo pcdelcampon@unal.edu.co

Examples

   data(DogBreeds)
   Fac.Num(DogBreeds)

   data(iris)
   Fac.Num(iris)

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

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

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:

Author(s)

Pedro Cesar del Campo pcdelcampon@unal.edu.co, Campo Elias Pardo cepardot@unal.edu.co, 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

# 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(DogBreeds)

DB.act <- DogBreeds[-7]  # active variables
DB.ilu <- DogBreeds[7]   # ilustrative variables

FC.db <-FactoClass( DB.act, dudi.acm, k.clust = 4,
                       scanFC = FALSE, dfilu = DB.ilu, nfcl = 10)

FC.db

FC.db$clus.summ
FC.db$indices

Table of Coordinates, Aids of Interpretation of the Principal Axes and Cluster Analysis in LaTeX format.

Description

The coordinates, aids of interpretation and results of cluster analysis of an object of class FactoClass are written in tables for edition in LaTeX format and written in a file.

Usage

FactoClass.tex(FC,job="",append=TRUE, dir = getwd(), p.clust = FALSE )

## S3 method for class 'FactoClass.tex'
print(x, ...)

latexDF(obj, job="latex" ,tit="" ,lab="" ,append=TRUE ,dec=1,
                     dir = getwd() , to.print = TRUE )
roundDF(tabla,dec=1)

    

Arguments

Details

This function helps with the construction of tables in LaTeX format. Besides, it allows a easy reading of the generated results by FactoClass. The function latexDF is an entrance to xtable and turns an object of class data.frame a table in LaTeX format.

Value

object of class FactoClass.tex with the following characteristics:

Author(s)

Pedro Cesar del Campo pcdelcampon@unal.edu.co, Campo Elias Pardo cepardot@unal.edu.co

Examples

data(DogBreeds)
DB.act <- DogBreeds[-7]  # active variables
DB.ilu <- DogBreeds[7]   # illustrative variables
# MCA
FaCl <- FactoClass( DB.act, dudi.acm,
                    scanFC = FALSE, dfilu = DB.ilu, nfcl = 10, k.clust = 4 )
# In order to create a file in LaTeX format 
# FactoClass.tex(FaCl,job="DogBreeds1", append=TRUE)
# FactoClass.tex(FaCl,job="DogBreeds", append=TRUE , p.clust = TRUE)

Student opinions about the Vietnam War

Description

The newspaper of the students of the University of Chapel Hill (North Carolina) conducted a survey of student opinions about the Vietnam War in May 1967. Responses were classified by sex, year in the program and one of four opinions:

A

defeat power of North Vietnam by widespread bombing and land invasion

B

follow the present policy

C

withdraw troops to strong points and open negotiations on elections involving the Viet Cong

D

immediate withdrawal of all U.S. troops

Usage

data(Vietnam)

Format

The 3147 consulted students were classified considering the sex, year of study and chosen strategy, originating a contingency table of 10 rows: M1 to M5 and F1 to F5 (the years of education are from 1 to 5 and sexes are male (M) and female (F)) and 4 columns A, B, C and D.

Source

Fine, J. (1996), 'Iniciacion a los analisis de datos multidimensionales a partir de ejemplos', Notes of course, Montevideo

Whisky example

Description

Data frame with five features of 35 whisky brands:

price

in Frace Francs

malt

proportion in percentage

type

by malt proportion: low, medium, pure

aging

in years

taste

mean score of a taste panel

Usage

data(Whisky)

Source

Fine, J. (1996), 'Iniciacion a los analisis de datos multidimensionales a partir de ejemplos', Notes of course, Montevideo

Add grids to a scatterplot3d (modified)

Description

The goal of this function is to add grids on an existing plot created using the package scatterplot3d

Usage

addgrids3d(x, y = NULL, z = NULL, grid = TRUE, col.grid = "grey",
  lty.grid = par("lty"), lab = par("lab"), lab.z = mean(lab[1:2]),
  scale.y = 1, angle = 40, xlim = NULL, ylim = NULL, zlim = NULL)

Arguments

Note

Users who want to extend an existing scatterplot3d graphic with the function addgrids3d, should consider to set the arguments scale.y, angle, ..., to the value used in scatterplot3d.

Author(s)

Alboukadel Kassambara alboukadel.kassambara@gmail.com

References

http://www.sthda.com

Examples

library(scatterplot3d)
data(iris)
scatterplot3d(iris[, 1:3], pch = 16, grid=TRUE, box=FALSE)
addgrids3d(iris[, 1:3], grid = c("xy", "xz", "yz"))

Admitted students to the seven careers of the Science Faculty

Description

Score obtained by each of the 445 students admitted to the seven careers of the Facultad de Ciencias of the Universidad Nacional de Colombia Bogota to the first semester of 2013, and some socio demographic information:

carr

a factor with the careers as its levels

mate,cien,soci,text,imag

score achieved in each of the areas of the admission exam

exam

total score of the admission exam

gene

gender of the admitted

estr

socioeconomic stratum in 3 categories

orig

geographic origin of the admitted

edad

age of the admitted in categories

niLE

if the admitted requires nivelation in language

niMa

if the admitted requires nivelation in mathematics

estr

socioeconomic stratum in 7 categories

age

age of the admitted in years

Usage

data(admi)

Format

Object of class data.frame with 445 rows and 15 columns.

Source

SIA: Academic Information System

References

C.E. Pardo (2015). Estadística descriptiva multivariada. Universidad Nacional de Colombia. Facultad de Ciencias.

Cofee cups

Description

Results of the mesure of some properties of twelve coffe cups

Usage

data(cafe)

Format

Object of class data.frame with 12 rows and 16 columns.

Source

R. Duarte and M. Suarez and E. Moreno and P. Ortiz (1996). An\'alisis multivariado por componentes principales, de caf\'es tostados y molidos adulterados con cereales. Cenicaf\'e, 478(2):65-76

References

C.E. Pardo (2023). Estadistica descriptiva multivariada. Universidad Nacional de Colombia. Facultad de Ciencias.

Centroids of the Classes of a Partition

Description

It evaluates the centroids of a partition with the weights in rw

Usage

centroids(df,cl,rw=rep(1/nrow(df),nrow(df)))

Arguments

Value

Object of class list with the following:

Author(s)

Campo Elias Pardo cepardot@unal.edu.co

Examples

data(iris)
centroids(iris[,-5],iris[,5])

Chisquare tests of a qualitative variable by several qualitative variables

Description

Chisqure tests are performed for the contingency tables crossing a qualitative variable named cl and the qualitative variables present in columns from df

Usage

chisq.carac(df,cl,thr=2,decr=TRUE)

Arguments

Value

Matrix with the following columns:

Author(s)

Campo Elias Pardo cepardot@unal.edu.co

Examples

data(DogBreeds)
round(chisq.carac(DogBreeds[,-7],DogBreeds[,7]),3)
round(chisq.carac(DogBreeds[,-7],DogBreeds[,7],decr=FALSE),3)

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

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.

Value

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

Author(s)

Pedro Cesar del Campo pcdelcampon@unal.edu.co, Campo Elias Pardo cepardot@unal.edu.co, Mauricio Sadinle msadinleg@unal.edu.co

References

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

Examples

data(DogBreeds)
DB.act <- DogBreeds[-7]  # active variables
DB.function <- subset(DogBreeds,select=7)   
cluster.carac(DB.act,DB.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(DogBreeds)
attach(DogBreeds)
weig<-table(FUNC,WEIG)
weig<-data.frame(weig[,1],weig[,2],weig[,3])
cluster.carac(weig,  row.names(weig), "fr", 2) # frequency variables
detach(DogBreeds)

LaTeX Tables of Coordinates and Aids to Interpretation of Principal Axis Methods

Description

Coordinates and aids of interpretation are wrote in tabular environment of LaTeX inside a Table

Usage

dudi.tex(dudi,job="",aidsC=TRUE,aidsR=TRUE,append=TRUE)
latex(obj,job="latex",tit="",lab="",append=TRUE,dec=1)

Arguments

Details

latex function is used to builp up a table. The aids of interpretation are obtained with inertia.dudi of ade4. A file is wrote in the work directory (job.txt) with the following tables:

tvalp

eigenvalues

c1

eigenvectors

co

column coordinates

col.abs

column contributions in percentage

col.rel

quality of the representation of columns in percentage

col.cum

accumulated quality of the representation of columns in percentage/100

li

row coordinates

row.abs

row contributions in percent

row.rel

quality of the representation of rows in percentage

row.cum

accumulated quality of the representation of rows in percentage/100

Author(s)

Campo Elias PARDO cepardot@unal.edu.co

Examples

data(Bogota)
coa1 <- dudi.coa(Bogota[,2:7], scannf = FALSE)
# In order to create a file: Bogota.tex in LaTeX
# dudi.tex(coa1,job="Bogota")  

Departmenst by Levels of Schools in Colombia

Description

Contingency Table that classificaes the schools of Colombia by departments and level of the schools agree with the performance of its students.

Usage

data(icfes08)

Format

Object whit class data.frame of 29 rows and 12 columns.

Source

ICFES Colombia

References

C.E. Pardo, M. B\'ecue and J.E. Ortiz (2013). Correspondence Analysis of Contingency Tables with Subpartitions on Rowsand Columns. Revista Colombiana de Estad\'istica, 36(1):115-144.

K-means with Weights of the Elements

Description

It is a modification of kmeans Hartigan-Wong algorithm to consider the weight of the elements to classify.

Usage

kmeansW(x, centers, weight = rep(1,nrow(x)),
        iter.max = 10, nstart = 1)

Arguments

Details

With the 'Hartigan-Wong' algorithm, this function performs the K-means clustering diminishing inertia intra classes. In this version the Fortran code kmnsW.f was changed by C++ code kmeanw.cc programed by Camilo Jose Torres, modifing C code programed by Burkardt.

Value

object of class FactoClass.tex with the following characteristics:

Author(s)

Camilo Jose Torres cjtorresj@unal.edu.co, Campo Elias Pardo cepardot@unal.edu.co

References

Hartigan, J. A. and Wong, M. A. (1979). A K-means clustering algorithm. Applied Statistics 28, 100–108.

Burkardt, J. (2008). ASA136 The K-Means Algorithm. https://people.sc.fsu.edu/~jburkardt/cpp_src/asa136/asa136.html

Examples

 data(Bogota)
 ac.bog <- Bogota[-1]
 il.bog <- Bogota[ 1]
 
 acs <- dudi.coa( ac.bog, nf=6 , scannf = FALSE )
 
 kmeansW( acs$li, 7, acs$lw )
 

list to data.frame

Description

Modification of an object of class list into an object of class data.frame.

Usage

list.to.data(lista,nvar="clasif")

Arguments

Details

This function turns an object of class list into an object of class data.frame, this function is used internally to create objects of class data.frame to make tables in LaTeX format.

Value

Object of class data.frame.

Author(s)

Pedro Cesar Del Campo pcdelcampon@unal.edu.co

Examples

 A <- data.frame(r1=rnorm(5),r2=rnorm(5))
 B <- data.frame(r1=rnorm(15),r2=rnorm(15))

 LL <- list(A=A,B=B)
 LL
 list.to.data(LL)

Factorial Planes from Objects of Class dudi

Description

It plots factorial planes from objects of class dudi

Usage

## S3 method for class 'dudi'
plot(x,ex=1,ey=2,xlim=NULL,ylim=NULL,main=NULL,rotx=FALSE,
      roty=FALSE,roweti=row.names(dudi$li),
      coleti=row.names(dudi$co),axislabel=TRUE,font.col="plain",
      font.row="plain",col.row="black",col.col="blue",
      alpha.col=1,alpha.row=1,cex=0.8,cex.row=0.8,cex.col=0.8,
      all.point=TRUE,Trow=TRUE,Tcol=TRUE,cframe=1.2,ucal=0,
      cex.global=1,infaxes="out",gg=FALSE,...)
sutil.grid(cgrid,scale=TRUE)    

Arguments

Details

Plot the selected factorial plane. sutil.grid is used by plot.dudi

Value

It graphs the factorial plane x,y using $co, $li of a "dudi" object. If ucal > 0, the function inertia.dudi is used to calculate the quality of representation on the plane

Author(s)

Campo Elias Pardo cepardot@unal.edu.co and Jhonathan Medina jmedinau@unal.edu.co

Examples

data(Bogota)
ca <- dudi.coa(Bogota[,2:7],scannf=FALSE,nf=4)
# with ggplot2 and ggrepel
plot(ca,gg=TRUE)
dev.new()
# ade4 style
plot.dudi(ca,ex=3,ey=4,ucal=0.2,all.point=FALSE,infaxes="in")

Factorial Planes Showing the Classes

Description

For objects of class FactoClass it graphs a factorial plane showing the center of gravity of the cluster, and identifying with colors the cluster to which each element belongs.

Usage

plotFactoClass(FC,x=1,y=2,xlim=NULL,ylim=NULL,rotx=FALSE,roty=FALSE,
                roweti=row.names(dudi$li),coleti=row.names(dudi$co),
                titre=NULL,axislabel=TRUE,col.row=1:FC$k, 
                col.col="blue",cex=0.8,cex.row=0.8,cex.col=0.8,
                all.point=TRUE,Trow=TRUE,Tcol=TRUE,cframe=1.2,ucal=0,
                cex.global=1,infaxes="out",
                nclus=paste("cl", 1:FC$k, sep=""),
                cex.clu=cex.row,cstar=1,gg=FALSE)

Arguments

Details

It draws the factorial plane with the clusters. Only for objects FactoClass see FactoClass. The factorial plane is drawn with planfac and the classes are projected with s.class of ade4

Value

It draws the factorial plane x, y using $co, $li of the object of class FactoClass. If ucal > 0, the function inertia.dudi is used to calculate the quality of representation in the plane.

Author(s)

Campo Elias Pardo cepardot@unal.edu.co Pedro Cesar del Campo pcdelcampon@unal.edu.co,

Examples

data(Bogota)
Bog.act <- Bogota[-1]
Bog.ilu <- Bogota[ 1]

FC.Bogota<-FactoClass(Bog.act, dudi.coa,Bog.ilu,nf=2,nfcl=5,k.clust=5,scanFC=FALSE)

plotFactoClass(FC.Bogota,titre="First Factorial Plane from the SCA of Bogota's Blocks",
     col.row=c("maroon2","orchid4","darkgoldenrod2","dark red","aquamarine4"))

Correlation circle from coordinates

Description

It plots Correlation circle from a coordinate table

Usage

plotcc(x,ex=1,ey=2,cex.label=4.5,col.label="black",font.label="bold",col.arrow="black",
			fullcircle=TRUE,y=NULL)

Arguments

Details

Plot the selected factorial plane as a correlation circle for the variables from a normed PCA.

Value

It graphs the factorial plane ex,ey using a data.frame or matrix x with axis coordinates.

Author(s)

Jhonathan Medina jmedinau@unal.edu.co and Campo Elias Pardo cepardot@unal.edu.co

Examples

data(admi)
pca <- dudi.pca(admi[,2:6],scannf=FALSE,nf=2)
# fullcircle
plotcc(pca$co)
# no fullcircle
plotcc(pca$co,fullcircle=FALSE)

Row and Column Profiles of a Contingency Table

Description

It plots barplot profiles of rows or columns from a contingency table including marginal profiles

Usage

plotct(x,profiles="both",legend.text=TRUE,tables=FALSE,nd=1,... )

Arguments

Details

Plot row profiles in horizontal form and columns profiles in vertical form

Value

if tables=TRUE, object of class list with the following:

Author(s)

Camilo Jose Torres cjtorresj@unal.edu.co , Campo Elias Pardo cepardot@unal.edu.co

Examples

mycolors<-colors()[c(1,26,32,37,52,57,68,73,74,81,82,84,88,100)]
data(Bogota)
plotct(Bogota[,2:7],col=mycolors)
# return tables with marginals
tabs <- plotct(Bogota[,2:7],col=mycolors,tables=TRUE,nd=0)

Factorial Planes from Coordinates

Description

It plots factorial planes from a coordinate table

Usage

plotfp(co,x=1,y=2,eig=NULL,cal=NULL,ucal=0,xlim=NULL,ylim=NULL,main=NULL,rotx=FALSE,
       roty=FALSE,eti=row.names(co),axislabel=TRUE,col.row="black",cex=0.8,cex.row=0.8,
       all.point=TRUE,cframe=1.2,cex.global=1,infaxes="out",asp=1,gg=FALSE)

Arguments

Details

Plot the selected factorial plane.

Value

It graphs the factorial plane x,y using co and optional information of eigenvalues and representation quality of the points. If ucal > 0, only the points with the quality of representation on the plane bigger than ucal are pointed

Author(s)

Campo Elias Pardo cepardot@unal.edu.co and Jhonathan Medina jmedinau@unal.edu.co

Examples

data(Bogota)
ca <- dudi.coa(Bogota[,2:7],scannf=FALSE,nf=2)
# ade4 style
plotfp(ca$li,eig=ca$eig,main="First Factorial Plane",infaxes="in")
# with ggplot2 and ggrepel
plotfp(ca$li,eig=ca$eig,main="First Factorial Plane",gg=TRUE)

Modified pairs plot

Description

Modified pairs plot: marginal kernel densities in diagonal, bivariated kernel densities in triangular superior; and scatter bivariate plots in triangular inferior

Usage

plotpairs(X,maxg=5,cex=1)

Arguments

Details

Plot row profiles in horizontal form and columns profiles in vertical form

Value

The function does not return values

Author(s)

Campo Elias Pardo cepardot@unal.edu.co

Examples

data(iris)
plotpairs(iris[,-5])

Stable clusters for cluster analysis

Description

Performs Stable Cluster Algorithm for cluster analysis, using factorial coordinates from a dudi object

Usage

stableclus(dudi,part,k.clust,ff.clus=NULL,bplot=TRUE,kmns=FALSE) 

Arguments

Details

Diday (1972) (cited by Lebart et al. (2006)) presented a method for cluster analysis in an attempt to solve one of the inconvinients with the kmeans algorithm, which is convergence to local optims. Stable clusters are built by performing different partitions (using kmeansW algorithmn), each one with different initial points. The groups are then formed by selecting the individuals belonging to the same cluster in every partion.

Value

object of class stableclus with the following characteristics:

Author(s)

Carlos Andres Arias caariasr@unal.edu.co, Campo Elias Pardo cepardot@unal.edu.co

References

Arias, C. A.; Zarate, D.C. and Pardo C.E. (2009), 'Implementacion del metodo de grupos estables en el paquete FactoClass de R', in: XIX Simposio Colombiano de Estadistica. Estadisticas Oficiales Medellin Colombia, Julio 16 al 20 de 2009 Universidad Nacional de Colombia. Bogota.

Lebart, L. (2015), 'DtmVic: Data and Text Mining - Visualization, Inference, Classification. Exploratory statistical processing of complex data sets comprising both numerical and textual data.', Web. http://www.dtmvic.com/

Lebart, L., Morineau, A., Lambert, T. and Pleuvret, P. (1999), SPAD. Syst?me Pour L'Analyse des Don?es, Paris.

Lebart, L., Piron, M. and Morineau, A. (2006), Statisitique exploratoire multidimensionnelle. Visualisation et inference en fouilles de donnees, 4 edn, Dunod, Paris.

Examples

 data(ColorAdjective)
 FCcol <-FactoClass(ColorAdjective, dudi.coa,nf=6,nfcl=10,k.clust=7,scanFC = FALSE)
 acs <- FCcol$dudi
# stableclus(acs,3,3,4,TRUE,TRUE)

Projection of Qualitative Variables in PCA and MCA

Description

It returns the coordinates and aids to the interpretation when one or more qualitative variables are projected as ilustrative in PCA or MCA

Usage

supqual(du,qual)

Arguments

Value

object of class list with the following:

Author(s)

Campo Elias Pardo cepardot@unal.edu.co

Examples

# in PCA
data(admi)
Y<-admi[,2:6]
pcaY<-dudi.pca(Y,scannf=FALSE)
Yqual<-admi[,c(1,8)]
supqual(pcaY,Yqual)
# in MCA
Y<-admi[,c(8,11,9,10)]
mcaY<-dudi.acm(Y,scannf=FALSE)
supqual(mcaY,admi[,c(1,13)])

Hierarchic Classification by Ward's Method

Description

Performs the classification by Ward's method from the matrix of Euclidean distances.

Usage

ward.cluster(dista, peso = NULL , plots = TRUE, h.clust = 2, n.indi = 25 )

Arguments

Details

It is an entrance to the function h.clus to obtain the results of the procedure presented in Lebart et al. (1995). Initially the matrix of distances of Ward of the elements to classify is calculated:

The Ward's distance between two elements to classify $i$ and $l$ is given by:

W(i,l) = (m_i * m_l)/(m_i + m_i) * dist(i,l)^2

where $m_i$ y $m_l$ are the weights and $dist(i,l)$ is the Euclidean distance between them.

Value

It returns an object of class hclust and a table of level indices (depending of h.clust). If plots = TRUE it draws the indices of level and the dendrogram.

Author(s)

Pedro Cesar del Campo pcdelcampon@unal.edu.co, Campo Elias Pardo cepardot@unal.edu.co

References

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

Examples

data(ardeche)
ca <- dudi.coa(ardeche$tab,scannf=FALSE,nf=4)

 ward.cluster( dista= dist(ca$li), peso=ca$lw )

 dev.new()
 HW <- ward.cluster( dista= dist(ca$li), peso=ca$lw ,h.clust = 1)
 plot(HW)
 rect.hclust(HW, k=4, border="red")