Help for package conjoint

Version:

1.42

Title:

An Implementation of Conjoint Analysis Method

Description:

This is a simple R package that allows to measure the stated preferences using traditional conjoint analysis method.

License:

GPL-2 | GPL-3 [expanded from: GPL]

URL:

https://github.com/packagesR/conjoint

Imports:

AlgDesign, stats, grDevices, graphics, fpc, broom, ggplot2, cluster, ggfortify

Encoding:

UTF-8

NeedsCompilation:

Author:

Andrzej Bak [aut], Tomasz Bartlomowicz [aut, cre]

Maintainer:

Tomasz Bartlomowicz <tomasz.bartlomowicz@ue.wroc.pl>

Repository:

CRAN

Date:

2025-06-22

Packaged:

2025-06-09 21:58:49 UTC; tomasz.bartlomowicz

Date/Publication:

2025-06-09 22:20:02 UTC

Function Conjoint sums up the main results of conjoint analysis

Description

Function Conjoint is a combination of following conjoint pakage's functions: caPartUtilities, caUtilities and caImportance. Therefore it sums up the main results of conjoint analysis. Function Conjoint returns matrix of partial utilities for levels of variables for respondents, vector of utilities for attribute's levels and vector of percentage attributes' importance with corresponding chart (barplot). The sum of importance should be 100

Usage

Conjoint(y, x, z, y.type)

Arguments

y

matrix of preferences

x

matrix of profiles

z

matrix of levels names

y.type

type of data preferences (possible values: "score" for preferences as rating data, "rank" for preferences as ranking data; default value: y.type="score")

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

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)
data(ice)
print("Preferences of all respondents (preferences as ranking data):")
Conjoint(ipref,iprof,ilevn,y.type="rank")

#Example 2
library(conjoint)
data(ice)
ipref=caRankToScore(ipref)
print("Preferences of all respondents (preferences converted into rating data):")
Conjoint(ipref,iprof,ilevn,y.type="score")

#Example 3
library(conjoint)
data(journey)
print("Preferences of all respondents (preferences as default - rating data):")
Conjoint(jpref,jprof,jlevn)

#Example 4
library(conjoint)
data(tea)
print("Preferences of all respondents (preferences as rating data):")
Conjoint(tprefm,tprof,tlevn,y.type="score")

#Example 5
library(conjoint)
data(tea)
print("Preferences of first respondent (preferences as default - rating data):")
Conjoint(tprefm[1,],tprof,tlevn)

#Example 6
library(conjoint)
data(tea)
print("Preferences of group of 5 respondents (preferences as rating data):")
Conjoint(tprefm[11:15,],tprof,tlevn,y.type="score")

Function ShowAllSimulations sums up the results of all simulation functions

Description

Function ShowAllSimulations sums up the results of all simulation functions. It's a combination of following conjoint pakage's functions: caMaxUtility, caBTL and caLogit. Therefore it sums up the main results of simulation using conjoint analysis method. Function ShowAllSimulations returns three vectors of percentage participations using maximum utility, BTL and logit models. The sum of importance for every vector should be 100%.

Usage

ShowAllSimulations(sym, y, x)

Arguments

sym

matrix of simulation profiles

y

matrix of preferences

x

matrix of profiles

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
data(tea)
ShowAllSimulations(tsimp,tpref,tprof)

#Example 2
library(conjoint)
data(chocolate)
ShowAllSimulations(csimp,cpref,cprof)

#Example 3
#library(conjoint)
#data(journey)
#ShowAllSimulations(jsimp,jpref,jprof)

Function ShowAllUtilities sums up all results of utility measures

Description

Function ShowAllUtilities is a combination of following conjoint pakage's functions: caPartUtilities, caTotalUtilities, caUtilities and caImportance. Function ShowAllUtilities returns: matrix of partial utilities (basic matrix of utilities with the intercept), matrix of total utilities for n profiles and all respondents, vector of utilities for attribute's levels and vector of percentage attributes' importance, with sum of importance. The sum of importance should be 100%.

Usage

ShowAllUtilities(y, x, z)

Arguments

y

matrix of preferences

x

matrix of profiles

z

matrix of levles names

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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

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

Examples

#Example 1
library(conjoint)
data(tea)
ShowAllUtilities(tprefm,tprof,tlevn)

#Example 2
library(conjoint)
data(chocolate)
ShowAllUtilities(cprefm,cprof,clevn)

Function caBTL estimates participation (market share) of simulation profiles

Description

Function caBTL estimates participation of simulation profiles using probabilistic model BTL (Bradley-Terry-Luce). Function returns vector of percentage participations. The sum of participation should be 100%.

Usage

caBTL(sym, y, x)

Arguments

sym

matrix of simulation profiles

y

matrix of preferences

x

matrix of profiles

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
data(tea)
simutil<-caBTL(tsimp,tpref,tprof)
print("Percentage participation of profiles: ", quote=FALSE)
print(simutil)

#Example 2
library(conjoint)
data(chocolate)
simutil<-caBTL(csimp,cpref,cprof)
print("Percentage participation of profiles:", quote=FALSE)
print(simutil)

#Example 3
library(conjoint)
data(chocolate)
ShowAllSimulations(csimp,cpref,cprof)

#Example 4
#library(conjoint)
#data(journey)
#ShowAllSimulations(jsimp,jpref,jprof)

Function caEncodedDesign encodes full or fractional factorial design

Description

Function caEncodedDesign encodes full or fractional factorial design. Function converts design of experiment to matrix of profiles.

Usage

caEncodedDesign(design)

Arguments

design

design of experiment returned by caFactorialDesign function

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
experiment<-expand.grid(
price=c("low","medium","high"),
variety=c("black","green","red"),
kind=c("bags","granulated","leafy"),
aroma=c("yes","no"))
design=caFactorialDesign(data=experiment,type="orthogonal")
print(design)
code=caEncodedDesign(design)
print(code)
print(cor(code))

Function caFactorialDesign creates full or fractional factorial design

Description

Function caFactorialDesign creates full or fractional factorial design. Function can return orthogonal factorial design.

Usage

caFactorialDesign(data, type="null", cards=NA, seed=123)

Arguments

data

experiment whose design consists of two or more factors, each with with 2 or more discrete levels

type

type of factorial design (possible values: "full", "fractional", "ca", "aca", "orthogonal"; default value: type="null")

cards

number of experimental runs

seed

seed settings (default value: seed=123)

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
experiment<-expand.grid(
price=c("low","medium","high"),
variety=c("black","green","red"),
kind=c("bags","granulated","leafy"),
aroma=c("yes","no"))
design=caFactorialDesign(data=experiment,type="full")
print(design)
print(cor(caEncodedDesign(design)))

#Example 2
library(conjoint)
experiment<-expand.grid(
price=c("low","medium","high"),
variety=c("black","green","red"),
kind=c("bags","granulated","leafy"),
aroma=c("yes","no"))
design=caFactorialDesign(data=experiment)
print(design)
print(cor(caEncodedDesign(design)))

#Example 3
library(conjoint)
experiment<-expand.grid(
price=c("low","medium","high"),
variety=c("black","green","red"),
kind=c("bags","granulated","leafy"),
aroma=c("yes","no"))
design=caFactorialDesign(data=experiment,type="orthogonal")
print(design)
print(cor(caEncodedDesign(design)))

#Example 4
library(conjoint)
experiment<-expand.grid(
price=c("low","medium","high"),
variety=c("black","green","red"),
kind=c("bags","granulated","leafy"),
aroma=c("yes","no"))
design=caFactorialDesign(data=experiment,type="fractional",cards=16)
print(design)
print(cor(caEncodedDesign(design)))

#Example 5
library(conjoint)
experiment<-expand.grid(
price=c("low","medium","high"),
variety=c("black","green","red"),
kind=c("bags","granulated","leafy"),
aroma=c("yes","no"))
design=caFactorialDesign(data=experiment,type="fractional")
print(design)
print(cor(caEncodedDesign(design)))

#Example 6
library(conjoint)
experiment<-expand.grid(
price=c("low","medium","high"),
variety=c("black","green","red"),
kind=c("bags","granulated","leafy"),
aroma=c("yes","no"))
design=caFactorialDesign(data=experiment,type="ca")
print(design)
print(cor(caEncodedDesign(design)))

#Example 7
library(conjoint)
experiment<-expand.grid(
price=c("low","medium","high"),
variety=c("black","green","red"),
kind=c("bags","granulated","leafy"),
aroma=c("yes","no"))
design=caFactorialDesign(data=experiment,type="aca")
print(design)
print(cor(caEncodedDesign(design)))

Function caImportance calculates importance of all attributes

Description

Function caImportance calculates importance of all attributes. Function returns vector of percentage attributes' importance and corresponding chart (barplot). The sum of importance should be 100%.

Usage

caImportance(y, x)

Arguments

y

matrix of preferences

x

matrix of profiles

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
data(tea)
imp<-caImportance(tprefm,tprof)
print("Importance summary: ", quote=FALSE)
print(imp)
print(paste("Sum: ", sum(imp)), quote=FALSE)

#Example 2
library(conjoint)
data(chocolate)
imp<-caImportance(cprefm,cprof)
print("Importance summary: ", quote=FALSE)
print(imp)
print(paste("Sum: ", sum(imp)), quote=FALSE)

#Example 3
library(conjoint)
data(journey)
imp<-caImportance(jpref[1,],jprof)
print("Importance summary of first respondent: ", quote=FALSE)
print(imp)
print(paste("Sum: ", sum(imp)), quote=FALSE)

#Example 4
library(conjoint)
data(journey)
imp<-caImportance(jpref[1:5,],jprof)
print("Importance summary of group of 5 respondents: ", quote=FALSE)
print(imp)
print(paste("Sum: ", sum(imp)), quote=FALSE)

Function caLogit estimates participation (market share) of the simulation profiles

Description

Function caLogit estimates participation of simulation profiles using logit model. Function returns vector of percentage participations. The sum of participation should be 100%.

Usage

caLogit(sym, y, x)

Arguments

sym

matrix of simulation profiles

y

matrix of preferences

x

matrix of profiles

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
data(tea)
simutil<-caLogit(tsimp,tpref,tprof)
print("Percentage participation of profiles: ", quote=FALSE)
print(simutil)

#Example 2
library(conjoint)
data(chocolate)
simutil<-caLogit(csimp,cpref,cprof)
print("Percentage participation of profiles:", quote=FALSE)
print(simutil)

#Example 3
library(conjoint)
data(chocolate)
ShowAllSimulations(csimp,cpref,cprof)

#Example 4
#library(conjoint)
#data(journey)
#ShowAllSimulations(jsimp,jpref,jprof)

Function caMaxUtility estimates participation (market share) of simulation profiles

Description

Function caMaxUtility estimates participation of simulation profiles using model of maximum utility ("first position"). Function returns vector of percentage participations. The sum of participation should be 100%.

Usage

caMaxUtility(sym, y, x)

Arguments

sym

matrix of simulation profiles

y

matrix of preferences

x

matrix of profiles

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
data(tea)
simutil<-caMaxUtility(tsimp,tpref,tprof)
print("Percentage participation of profiles: ", quote=FALSE)
print(simutil)

#Example 2
library(conjoint)
data(chocolate)
simutil<-caMaxUtility(csimp,cpref,cprof)
print("Percentage participation of profiles:", quote=FALSE)
print(simutil)

#Example 3
library(conjoint)
data(chocolate)
ShowAllSimulations(csimp,cpref,cprof)

#Example 4
#library(conjoint)
#data(journey)
#ShowAllSimulations(jsimp,jpref,jprof)

Function caModel estimates parameters of conjoint analysis model

Description

Function caModel estimates parameters of conjoint analysis model for one respondent. Function caModel returns vector of estimated parameters of traditional conjoint analysis model.

Usage

caModel(y, x)

Arguments

y

vector of preferences, vector should be like single profil of preferences

x

matrix of profiles

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
data(tea)
model=caModel(tprefm[1,], tprof)
print(model)

#Example 2
library(conjoint)
data(chocolate)
model=caModel(cprefm[1,], cprof)
print(model)

#Example 3
library(conjoint)
data(journey)
model=caModel(jpref[306,], jprof)
print(model)

Function caPartUtilities calculates matrix of individual utilities

Description

Function caPartUtilities calculates matrix of individual utilities for respondents. Function returns matrix of partial utilities (parameters of conjoint model regresion) for all artificial variables including parameters for reference levels for respondents (with intercept on first place).

Usage

caPartUtilities(y, x, z)

Arguments

y

matrix of preferences

x

matrix of profiles

z

vector of levels names

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
data(tea)
uslall<-caPartUtilities(tprefm,tprof,tlevn)
print(uslall)

#Example 2
library(conjoint)
data(chocolate)
uslall<-caPartUtilities(cprefm,cprof,clevn)
print(head(uslall))

#Example 3
library(conjoint)
data(journey)
usl<-caPartUtilities(jpref[1,],jprof,jlevn)
print("Individual (partial) utilities for first respondent:")
print(usl)

Function caRankToScore transforms ranking data into rating data design

Description

Function caRankToScore transforms ranking data into rating data design necessary for conjoint model.

Usage

caRankToScore(y.rank)

Arguments

y.rank

matrix of preferences in ranking format

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
data(ice)
print(ilevn)
print(iprof)
print(ipref)
preferences<-caRankToScore(ipref)
print(preferences)
Conjoint(preferences, iprof, ilevn)

Function caRecreatedDesign reconstructs factorial design

Description

Function caRecreatedDesign reconstructs the factorial design on the basis of arguments in the form of: a vector of variables (attributes) names, a vector of the number of variables' levels, a vector of variable level names and the list of numbers of the reconstructed profiles.

Usage

caRecreatedDesign(attr.names,lev.numbers,z,prof.numbers)

Arguments

attr.names

a vector of variables (attributes) names

lev.numbers

a vector of the number of variables' levels

z

a vector of variable level names

prof.numbers

list of numbers of the reconstructed profiles

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
attrNames<-c("price","variety","kind","aroma")
levNumbers<-c(3,3,3,2)
z<-c("low","medium","high","black","green","red","bags","granulated","leafy","yes","no")
profNumbers<-c(3,4,14,20,27,29,33,35,39,43,46,50,51)
design<-caRecreatedDesign(attrNames,levNumbers,z,profNumbers)
print(design)
print(design$dnumbers)
print(design$dnames)

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 andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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')

Function caTotalUtilities calculates matrix of theoreticall total utilities

Description

Function caTotalUtilities calculates matrix of theoreticall total utilities for respondents. Function returns matrix of total utilities for all profiles.

Usage

caTotalUtilities(y, x)

Arguments

y

matrix of preferences

x

matrix of profiles

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
data(tea)
uslall<-caTotalUtilities(tprefm,tprof)
print(uslall)

#Example 2
library(conjoint)
data(chocolate)
uslall<-caTotalUtilities(cprefm,cprof)
print(uslall)

#Example 3
library(conjoint)
data(journey)
usl<-caTotalUtilities(jpref[1,],jprof)
print("Individual (total) utilities for first respondent:")
print(usl)

Function caUtilities calculates utilities of levels of atrtributes

Description

Function caUtilities calculates utilities of attribute's levels. Function returns vector of utilities.

Usage

caUtilities(y,x,z)

Arguments

y

matrix of preferences

x

matrix of profiles

z

matrix of levels names

Author(s)

Andrzej Bak andrzej.bak@ue.wroc.pl,

Tomasz Bartlomowicz tomasz.bartlomowicz@ue.wroc.pl

Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

References

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)
data(tea)
uslall<-caUtilities(tprefm,tprof,tlevn)
print(uslall)

#Example 2
library(conjoint)
data(chocolate)
uslall<-caUtilities(cprefm,cprof,clevn)
print(uslall)

#Example 3
library(conjoint)
data(journey)
usl<-caUtilities(jpref[1,],jprof,jlevn)
print("Individual utilities for first respondent:")
print(usl)

Sample data for conjoint analysis

Description

Sample data in score mode. Rating (score) data does not need any conversion. Data collected in the survey conducted by W. Nowak in 2000.

Usage

data(chocolate)

Format

cpref: Vector of preferences (length 1392).
cprefm: Matrix of preferences (87 respondents and 16 profiles).
cprof: Matrix of profiles (5 attributes and 16 profiles).
clevn: Character vector of names for the attributes' levels.
csimp: Matrix of simulation profiles.

Examples

library(conjoint)
data(chocolate)
print(cprefm)
print(cprof)
print(clevn)
print(csimp)

Sample data for conjoint analysis

Description

Sample data in score mode. Rating (score) data does not need any conversion. Data collected in the survey conducted by W. Nowak in 2000.

Usage

data(czekolada)

Format

czpref: Vector of preferences (length 1392).
czprefm: Matrix of preferences (87 respondents and 16 profiles).
czprof: Matrix of profiles (5 attributes and 16 profiles).
czlevn: Character vector of names for the attributes' levels.
czsimp: Matrix of simulation profiles.

Examples

library(conjoint)
data(czekolada)
print(czprefm)
print(czprof)
print(czlevn)
print(czsimp)

Sample data for conjoint analysis

Description

Sample data in score mode. Rating (score) data does not need any conversion. Data collected in the survey conducted by M. Baran in 2007.

Usage

data(herbata)

Format

hpref: Vector of preferences (length 1300).
hprefm: Matrix of preferences (100 respondents and 13 profiles).
hprof: Matrix of profiles (4 attributes and 13 profiles).
hlevn: Character vector of names for the attributes' levels.
hsimp: Matrix of simulation profiles.

Examples

library(conjoint)
data(herbata)
print(hprefm)
print(hprof)
print(hlevn)
print(hsimp)

Sample data for conjoint analysis

Description

Sample artificial data in rank mode. Ranking (rank) data needs conversion into rating (score) data.

Usage

data(ice)

Format

ipref: Matrix of preferences (6 respondents and 9 profiles).
iprof: Matrix of profiles (4 attributes and 9 profiles).
ilevn: Character vector of names for the attributes' levels.

Examples

library(conjoint)
data(ice)
print(ipref)
print(iprof)
print(ilevn)

Sample data for conjoint analysis

Description

Sample data in score mode. Rating (score) data does not need any conversion. Data collected in the survey conducted by M. Gordzicz in 2015/2016.

Usage

data(journey)

Format

jpref: Matrix of preferences (306 respondents and 14 profiles).
jprof: Matrix of profiles (4 attributes and 14 profiles).
jlevn: Character vector of names for the attributes' levels.
jsimp: Matrix of simulation profiles.

Examples

library(conjoint)
data(journey)
print(jpref)
print(jprof)
print(jlevn)
print(jsimp)

Sample data for conjoint analysis

Description

Sample artificial data in rank mode. Ranking (rank) data needs conversion into rating (score) data.

Usage

data(lody)

Format

lpref: Matrix of preferences (6 respondents and 9 profiles).
lprof: Matrix of profiles (4 attributes and 9 profiles).
llevn: Character vector of names for the attributes' levels.

Examples

library(conjoint)
data(lody)
print(lpref)
print(lprof)
print(llevn)

Sample data for conjoint analysis

Description

Sample artificial data in score mode. Rating (score) data does not need any conversion.

Usage

data(plyty)

Format

ppref: Matrix of preferences (6 respondents and 8 profiles).
pprof: Matrix of profiles (3 attributes and 8 profiles).
plevn: Character vector of names for the attributes' levels.

Examples

library(conjoint)
data(plyty)
print(ppref)
print(pprof)
print(plevn)

Sample data for conjoint analysis

Description

Sample data in score mode. Rating (score) data does not need any conversion. Data collected in the survey conducted by M. Baran in 2007.

Usage

data(tea)

Format

tpref: Vector of preferences (length 1300).
tprefm: Matrix of preferences (100 respondents and 13 profiles).
tprof: Matrix of profiles (4 attributes and 13 profiles).
tlevn: Character vector of names for the attributes' levels.
tsimp: Matrix of simulation profiles.

Examples

library(conjoint)
data(tea)
print(tprefm)
print(tprof)
print(tlevn)
print(tsimp)

Sample data for conjoint analysis

Description

Sample data in score mode. Rating (score) data does not need any conversion. Data collected in the survey conducted by M. Gordzicz in 2015/2016.

Usage

data(wycieczka)

Format

wpref: Matrix of preferences (306 respondents and 14 profiles).
wprof: Matrix of profiles (4 attributes and 14 profiles).
wlevn: Character vector of names for the attributes' levels.
wsimp: Matrix of simulation profiles.

Examples

library(conjoint)
data(wycieczka)
print(wpref)
print(wprof)
print(wlevn)
print(wsimp)

Function Conjoint sums up the main results of conjoint analysis

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Function ShowAllSimulations sums up the results of all simulation functions

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Function ShowAllUtilities sums up all results of utility measures

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Function caBTL estimates participation (market share) of simulation profiles

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Function caEncodedDesign encodes full or fractional factorial design

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Function caFactorialDesign creates full or fractional factorial design

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Function caImportance calculates importance of all attributes

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Function caLogit estimates participation (market share) of the simulation profiles

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Function caMaxUtility estimates participation (market share) of simulation profiles

Description

Usage

Arguments

Author(s)

References

See Also

Examples

Function caModel estimates parameters of conjoint analysis model

Description

Usage

Arguments

Author(s)

References

See Also

Examples