caImportance | R Documentation |
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%.
caImportance(y, x)
y |
matrix of preferences |
x |
matrix of profiles |
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
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.
Conjoint
#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)
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