如何在R中解释choicemodelr（rhierMnlRwMixture）的输出

Question

My Problem

I just started using the R library 'choicemodelr' and succeded in getting some beta values as a solution. But I wonder how do I assign these values to the specific attribute-levels. As a result I only get values for A1B1, A1B2, A1B3,... etc. How does this generic output generally connect to my Design?

Didn't find a hint in the documentation. Neither for the choicemodelr libraray, nor the bayesm library (rhierMnlRwMixture) to which it is connected to. I hope you can help me with this one.

Thanks in advance, Phil

to illustrate this, some code and output:

my code in R

# loading neccesary librarys
library(bayesm)
library(MASS)
library(lattice)
library(Matrix)
library(ChoiceModelR)
library(XLConnect)

#DATENSATZ:
setwd("C:/DATA/CBC/")   # set workingdirectory
.Workbook <- loadWorkbook("DataCBC-R2.xls")
data <- data.frame(readWorksheet(.Workbook, "DataCBC-R"))
remove(.Workbook)

#set parameter for calculation
R = 50000 #Total Iterations of the Markov Chain Monte Carlo
use = 100 #Iterations for Paramerter-Estimation

#Parameter of datainput
none = TRUE #TRUE, if the questionaire has a none-Option but is not coded in the data
xcoding = c(0,0,0,0,0,0,1,1) #0=nominal scale; 1=metric scale

#Parameter dataoutput
save = TRUE #TRUE saves the calculated parameters
keep = 500 #number of random parameter draws to save (thinnig Parameter)

mcmc = list (R=R, use=use)
options = list(none=none, save=save, keep=keep, restart=restart)

#final calculation of the betas
out = choicemodelr(data, xcoding, mcmc = mcmc, demos = demos, options = options, constraints = constraints)

I get the following Output (excerpt):

Resp A1B1 A1B2 A1B3 A1B4 A2B1 A2B2 NONE
001 -2,56 -6,54 -18,49 27,59 -1,74 1,74 -1,94
002 -3,18 -6,52 -19,79 29,49 0,50 -0,50 -0,58

Hope that piece of information helps. Let me know, if you need further information.

Answer 1

Here is the input data:

respondent  choice-set  stimulus   Attr. Color  Attr. Shape  Choice
1           1           1          1            2            1
1           1           2          3            1            0
1           1           3          2            1            0
1           2           1          1            2            4
1           2           2          3            1            0
1           2           3          2            1            0
1           3           1          4            1            3
1           3           2          1            2            0
1           3           3          3            1            0
1           4           1          2            1            2
...        ...         ...        ...          ...          ...

Answer 2

在交叉验证 （首先应该在此处）处找到完整答案： https : //stats.stackexchange.com/questions/85362/how-to-interpret-the-output-of-choicemodelr-rhiermnlrwmixture-in -r / 86102＃86102