How to interpret the output of choicemodelr (rhierMnlRwMixture) in R 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? 
I didn't find a hint in the documentation. Neither for the choicemodelr library, nor the bayesm library (rhierMnlRwMixture) to which it is connected to. I hope you can help me with this one.

To illustrate this, some code and output:  
my design
Attribute   Attribute-Levels
Color          red, blue, yellow, green 
shape         square, round
...                ...
my data-input
respondent  choice-set  stimulus   Attr. Color  Attr. Shape  Choice
1           1           1          blue         square       1
1           1           2          green        round        0
1           1           3          red          round        0
1           2           1          blue         square       4
1           2           2          green        round        0
1           2           3          red          round        0
1           3           1          yellow       round        3
1           3           2          blue         square       0
1           3           3          green        round        0
1           4           1          red          round        2
...        ...         ...        ...          ...          ...

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):
RespID  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

So, which of the A1 betas refers to which color?
 A: Just had a conversation with the author/maintainer of choicemodelR. He gave me the following explanation: 

The data must be numeric only, i.e. character variables would not work.  Also, the variables should not be factors, although it might take those as inputs[...]. The categorical attributes must be integers starting with "1".  Then you would have to link up the numbers to the text description of each level outside of choicemodelR.

With this coding the output attribute-levels A1B1, A1B2, etc corresponds with the input format 1,2,...
So the input has to look s.th. like that (compare with the input above):
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
...        ...         ...        ...          ...          ...

