conjoint (version 1.41)

caImportance: 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

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

Conjoint

Examples

# NOT RUN {
#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)
# }