caModel: Function caModel estimates parameters of conjoint analysis...

In conjoint: An Implementation of Conjoint Analysis Method

Description

Function caModel estimates parameters of conjoint analysis model for one respondent. Function caModel returns vector of estimated parameters of traditional conjoint analysis model.

Usage

caModel(y, x)

Arguments

y	vector of preferences, vector should be like single profil of preferences
x	matrix of profiles

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

Conjoint

Examples

#Example 1
library(conjoint)
data(tea)
model=caModel(tprefm[1,], tprof)
print(model)

#Example 2
library(conjoint)
data(chocolate)
model=caModel(cprefm[1,], cprof)
print(model)

#Example 3
library(conjoint)
data(journey)
model=caModel(jpref[306,], jprof)
print(model)

Example output

Call:
lm(formula = frml)

Residuals:
      1       2       3       4       5       6       7       8       9      10 
 1.1345 -1.4897  0.3103 -0.2655  0.3103  0.1931  1.5931 -1.4310 -1.4310  1.1207 
     11      12      13 
 0.3690  1.1931 -1.6069 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)   
(Intercept)          3.3937     0.5439   6.240  0.00155 **
factor(x$price)1    -1.5172     0.7944  -1.910  0.11440   
factor(x$price)2    -1.1414     0.6889  -1.657  0.15844   
factor(x$variety)1  -0.4747     0.6889  -0.689  0.52141   
factor(x$variety)2  -0.6747     0.6889  -0.979  0.37234   
factor(x$kind)1      0.6586     0.6889   0.956  0.38293   
factor(x$kind)2     -1.5172     0.7944  -1.910  0.11440   
factor(x$aroma)1     0.6293     0.5093   1.236  0.27150   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.78 on 5 degrees of freedom
Multiple R-squared:  0.8184,	Adjusted R-squared:  0.5642 
F-statistic:  3.22 on 7 and 5 DF,  p-value: 0.1082


Call:
lm(formula = frml)

Residuals:
   Min     1Q Median     3Q    Max 
 -0.50  -0.25   0.00   0.25   0.75 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)         8.583e+00  1.596e-01  53.790 2.77e-09 ***
factor(x$kind)1     2.000e+00  2.500e-01   8.000 0.000203 ***
factor(x$kind)2    -2.000e+00  2.500e-01  -8.000 0.000203 ***
factor(x$kind)3     6.000e+00  2.500e-01  24.000 3.44e-07 ***
factor(x$price)1   -5.523e-16  1.925e-01   0.000 1.000000    
factor(x$price)2   -2.500e-01  2.257e-01  -1.108 0.310361    
factor(x$packing)1  2.443e-16  1.443e-01   0.000 1.000000    
factor(x$weight)1  -3.333e-01  1.925e-01  -1.732 0.133975    
factor(x$weight)2  -8.333e-02  2.257e-01  -0.369 0.724605    
factor(x$calorie)1 -1.000e+00  1.443e-01  -6.928 0.000448 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.5774 on 6 degrees of freedom
Multiple R-squared:  0.9941,	Adjusted R-squared:  0.9853 
F-statistic: 112.7 on 9 and 6 DF,  p-value: 5.374e-06


Call:
lm(formula = frml)

Residuals:
        1         2         3         4         5         6         7         8 
 2.192308 -2.009615  2.557692 -2.740385  0.346154 -0.355769  0.009615 -3.307692 
        9        10        11        12        13        14 
 2.394231 -1.442308  2.355769  0.740385 -0.346154 -0.394231 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)   
(Intercept)                4.9375     0.8685   5.685  0.00235 **
factor(x$purpose)1         1.3125     1.4003   0.937  0.39165   
factor(x$purpose)2        -0.4375     1.4003  -0.312  0.76733   
factor(x$purpose)3         1.7356     1.6158   1.074  0.33184   
factor(x$form)1            0.9375     0.8685   1.080  0.32966   
factor(x$season)1         -0.6923     0.8617  -0.803  0.45823   
factor(x$accommodation)1   1.3125     1.4003   0.937  0.39165   
factor(x$accommodation)2   0.7356     1.6158   0.455  0.66802   
factor(x$accommodation)3  -1.4375     1.4003  -1.027  0.35171   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.107 on 5 degrees of freedom
Multiple R-squared:  0.6034,	Adjusted R-squared:  -0.0311 
F-statistic: 0.951 on 8 and 5 DF,  p-value: 0.549

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