conjoint (version 1.42)

caRecreatedDesign: 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

Andrzej Bak ,

Tomasz Bartlomowicz

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.

See Also

caFactorialDesign and caEncodedDesign

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)