conjoint (version 1.41)

caFactorialDesign: 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)

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

caEncodedDesign and caRecreatedDesign

Examples

# NOT RUN {
#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)))
# }