caSegmentation {conjoint}

Function caSegmentation divides respondents on clusters

Description

Function caSegmentation divides respondents on n clusters (segments) using k-means method (function kmeans, package stats). There are two data sets used - matrix or vector of preferences and matrix of profiles.

Usage

caSegmentation(y, x, c)

Arguments

y

matrix of preferences

x

matrix of profiles

c

number of clusters (optional), default value c=2

Author(s)

Andrzej Bak [e-mail],

Tomasz Bartlomowicz [e-mail]

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland http://keii.ue.wroc.pl/conjoint

References

Bak A., Bartlomowicz T. (2012), Conjoint analysis method and its implementation in conjoint R package, [In:] Pociecha J., Decker R. (Eds.), Data analysis methods and its applications, C.H.Beck, Warszawa, p.239-248.

Bak A. (2009), Analiza Conjoint [Conjoint Analysis], [In:] Walesiak M., Gatnar E. (Eds.), Statystyczna analiza danych z wykorzystaniem programu R [Statistical Data Analysis using R], Wydawnictwo Naukowe PWN, Warszawa, p. 283-317.

Green P.E., Srinivasan V. (1978), Conjoint Analysis in Consumer Research: Issues and Outlook, "Journal of Consumer Research", September, 5, p. 103-123.

SPSS 6.1 Categories (1994), SPSS Inc., Chicago.

Examples

#Example 1
library(conjoint)
require(fpc)
data(tea)
segments<-caSegmentation(tprefm,tprof)
print(segments$seg)
plotcluster(segments$util,segments$sclu)

#Example 2
library(conjoint)
require(fpc)
data(tea)
segments<-caSegmentation(tpref,tprof,3)
print(segments$seg)
plotcluster(segments$util,segments$sclu)

#example 3
library(conjoint)
require(fpc)
require(broom)
require(ggplot2)
data(tea)
segments<-caSegmentation(tprefm,tprof,3)
dcf<-discrcoord(segments$util,segments$sclu)
assignments<-augment(segments$segm,dcf$proj[,1:2])
ggplot(assignments)+geom_point(aes(x=X1,y=X2,color= .cluster))+labs(color="Cluster Assignment",
title="K-Means Clustering Results")

#Example 4
library(conjoint)
require(ggfortify)
data(tea)
segments<-caSegmentation(tpref,tprof,3)
print(segments$seg)
util<-as.data.frame(segments$util)
set.seed(123)
ggplot2::autoplot(kmeans(util,3),data=util,label=TRUE,label.size=4,frame=TRUE)

#Example 5
#library(conjoint)
#require(ggfortify)
#require(cluster)
#data(tea)
#segments<-caSegmentation(tpref,tprof,3)
#print(segments$seg)
#util<-as.data.frame(segments$util)
#ggplot2::autoplot(pam(util,3),label=TRUE,label.size=4,frame=TRUE,frame.type='norm')