caUtilities: Function caUtilities calculates utilities of levels of...

In conjoint: An Implementation of Conjoint Analysis Method

Description

Function caUtilities calculates utilities of attribute's levels. Function returns vector of utilities.

Usage

caUtilities(y,x,z)

Arguments

y	matrix of preferences
x	matrix of profiles
z	matrix of levels names

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

caPartUtilities and caTotalUtilities

Examples

#Example 1
library(conjoint)
data(tea)
uslall<-caUtilities(tprefm,tprof,tlevn)
print(uslall)

#Example 2
library(conjoint)
data(chocolate)
uslall<-caUtilities(cprefm,cprof,clevn)
print(uslall)

#Example 3
library(conjoint)
data(journey)
usl<-caUtilities(jpref[1,],jprof,jlevn)
print("Individual utilities for first respondent:")
print(usl)

Example output

Call:
lm(formula = frml)

Residuals:
    Min      1Q  Median      3Q     Max 
-5,1888 -2,3761 -0,7512  2,2128  7,5134 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)         3,55336    0,09068  39,184  < 2e-16 ***
factor(x$price)1    0,24023    0,13245   1,814    0,070 .  
factor(x$price)2   -0,14311    0,11485  -1,246    0,213    
factor(x$variety)1  0,61489    0,11485   5,354 1,02e-07 ***
factor(x$variety)2  0,03489    0,11485   0,304    0,761    
factor(x$kind)1     0,13689    0,11485   1,192    0,234    
factor(x$kind)2    -0,88977    0,13245  -6,718 2,76e-11 ***
factor(x$aroma)1    0,41078    0,08492   4,837 1,48e-06 ***
---
Signif. codes:  0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1

Residual standard error: 2,967 on 1292 degrees of freedom
Multiple R-squared:  0,09003,	Adjusted R-squared:  0,0851 
F-statistic: 18,26 on 7 and 1292 DF,  p-value: < 2,2e-16

dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")
 [1]  3.55336207  0.24022989 -0.14311494 -0.09711494  0.61488506  0.03488506
 [7] -0.64977011  0.13688506 -0.88977011  0.75288506  0.41077586 -0.41077586

Call:
lm(formula = frml)

Residuals:
     Min       1Q   Median       3Q      Max 
-11,3305  -3,5546   0,4799   3,4799   9,8190 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)         8,68487    0,12648  68,667  < 2e-16 ***
factor(x$kind)1    -1,08908    0,19815  -5,496 4,62e-08 ***
factor(x$kind)2    -0,73276    0,19815  -3,698 0,000226 ***
factor(x$kind)3    -0,92241    0,19815  -4,655 3,55e-06 ***
factor(x$price)1   -0,57088    0,15254  -3,743 0,000190 ***
factor(x$price)2    0,11877    0,17887   0,664 0,506777    
factor(x$packing)1 -0,02874    0,11440  -0,251 0,801714    
factor(x$weight)1  -0,16858    0,15254  -1,105 0,269272    
factor(x$weight)2   0,17337    0,17887   0,969 0,332575    
factor(x$calorie)1 -0,64655    0,11440  -5,652 1,93e-08 ***
---
Signif. codes:  0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1

Residual standard error: 4,268 on 1382 degrees of freedom
Multiple R-squared:  0,1488,	Adjusted R-squared:  0,1433 
F-statistic: 26,85 on 9 and 1382 DF,  p-value: < 2,2e-16

dev.new(): using pdf(file="Rplots4.pdf")
dev.new(): using pdf(file="Rplots5.pdf")
dev.new(): using pdf(file="Rplots6.pdf")
dev.new(): using pdf(file="Rplots7.pdf")
dev.new(): using pdf(file="Rplots8.pdf")
 [1]  8.684865900 -1.089080460 -0.732758621 -0.922413793  2.744252874
 [6] -0.570881226  0.118773946  0.452107280 -0.028735632  0.028735632
[11] -0.168582375  0.173371648 -0.004789272 -0.646551724  0.646551724

Call:
lm(formula = frml)

Residuals:
       1        2        3        4        5        6        7        8 
-3,19231  2,75962 -0,05769  0,49038  0,65385  0,10577 -0,75962  3,30769 
       9       10       11       12       13       14 
-1,14423 -0,05769 -2,10577 -2,49038 -0,65385  3,14423 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)   
(Intercept)                4,9375     0,9037   5,464   0,0028 **
factor(x$purpose)1        -0,9375     1,4572  -0,643   0,5483   
factor(x$purpose)2        -2,6875     1,4572  -1,844   0,1245   
factor(x$purpose)3         3,6394     1,6814   2,165   0,0827 . 
factor(x$form)1           -1,5625     0,9037  -1,729   0,1444   
factor(x$season)1          0,6923     0,8967   0,772   0,4750   
factor(x$accommodation)1   0,0625     1,4572   0,043   0,9674   
factor(x$accommodation)2   1,6394     1,6814   0,975   0,3743   
factor(x$accommodation)3   0,3125     1,4572   0,214   0,8387   
---
Signif. codes:  0 '***' 0,001 '**' 0,01 '*' 0,05 '.' 0,1 ' ' 1

Residual standard error: 3,233 on 5 degrees of freedom
Multiple R-squared:  0,7634,	Adjusted R-squared:  0,3849 
F-statistic: 2,017 on 8 and 5 DF,  p-value: 0,2281

dev.new(): using pdf(file="Rplots9.pdf")
dev.new(): using pdf(file="Rplots10.pdf")
dev.new(): using pdf(file="Rplots11.pdf")
dev.new(): using pdf(file="Rplots12.pdf")
[1] "Individual utilities for first respondent:"
 [1]  4.93750000 -0.93750000 -2.68750000  3.63942308 -0.01442308 -1.56250000
 [7]  1.56250000  0.69230769 -0.69230769  0.06250000  1.63942308  0.31250000
[13] -2.01442308

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