caBTL: Function caBTL estimates participation (market share) of...

In conjoint: An Implementation of Conjoint Analysis Method

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 [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.

See Also

caLogit, caMaxUtility and ShowAllSimulations

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)

Example output

[1] Percentage participation of profiles: 
[1] 18.84884 28.95980 30.13618 22.05518
[1] Percentage participation of profiles:
[1] 31.39379 26.43264 23.16542 19.00816
  TotalUtility MaxUtility BTLmodel LogitModel
1        10,41      52,87    31,39      49,82
2         8,50      22,99    26,43      25,21
3         7,39      11,49    23,17      12,66
4         6,35      12,64    19,01      12,32

conjoint documentation built on May 1, 2019, 8:05 p.m.