I know my reply comes rather late and most of the relevant points have
already been made.

To summarize:
1. You can use Sawtooth to run latent class segmentation. But you'll have to
have to buy the necessary module.
2. You can use Sawtooth to run regular HB estimation and then use
individual-level utilties for segmentation, but it's very important to
remember that you cannot use the raw utilities from Sawtooth output, as
Ralph mentioned. You should rescale them. It's simple but involves several
steps. I'll be happy to provide the exact algorithm.
3. If you want to estimate individual-level utilities for free, you can use
bayesm package in R. I've done it using rhierMnlRwMixture, as Ralph
mentioned. But one needs to format the design shown and the responses
(choices) in a certain way. If you want to try it - you could ask questions
here and we'll be happy to help. However, if you have a very large design
and a lot of respondents, you might need a powerful PC to be able to run it
in bayesm.

Dimitri Liakhovitski


On Wed, Feb 2, 2011 at 10:36 AM, Wirth, Ralph (GfK SE)
<ralph.wirth@gfk.com>wrote:

> When segmenting based on utility parameters derived from a choice model,
> you should probably rescale these utilities before clustering based on them,
> in order to account for differences in error variance. For example, you
> could use Sawtooth Software's zero-centered diffs transformation.
>
> As to HB-logit in bayesm: The function "rhierMnlRwMixture" is a very
> flexible function for estimating individual MNL parameters. However, as
> Chris pointed out, data preparation can be cumbersome and requires solid R
> programming skills. Also, the transformation and interpretation of the
> output is not that straightforward.
> >From my point of view, you could also modify rhierBinLogit (replace the
> binary logit likelihood function by an MNL Likelihood function). The code of
> rhierBinLogit is a bit easier to understand in my eyes, but of course you
> need both R programming and HB knowledge for using/modifying this function,
> too.
>
>
>
>
> -----Ursprüngliche Nachricht-----
> Von: r-sig-dcm-bounces@r-project.org [mailto:
> r-sig-dcm-bounces@r-project.org] Im Auftrag von Chris Chapman
> Gesendet: Dienstag, 1. Februar 2011 16:31
> An: walt@dataanalyticscorp.com; r-sig-dcm@r-project.org
> Betreff: Re: [R-sig-DCM] segmenting consumers after a dcm
>
> Yes, segmentation on HB estimated utilities is a common thing to do. There
> are some theoretical issues (for instance, HB assumes that a distribution
> applies across the population from which respondents are sampled, and this
> assumption at least partially conflicts with some ways in which segments may
> be conceived), and practical issues (they may be difficult to find).  In
> principle, however, HB utilities are probably as good as many other basis
> variables.
>
> As for R, yes, the bayesm package can estimate an HB model in R for many
> kinds of choice models. However, it has very specific requirements for how
> data must be formatted (as lists of difference-coded vectors). In my
> experience, translating data into its format and verifying that it is
> correctly in that format has been a non-trivial exercise for one-off
> projects. (I've started work on code to help do that automatically but it is
> incomplete and probably won't be done for quite a while.)
>
> If you want to make bayesm work, best bet is to check out its documentation
> in the package vignette, and also the appendices to the book by Rossi,
> Allenby, & McCulloch: Bayesian statistics and marketing (2005). If you're
> lucky enough to have data in a format close to theirs, it's not difficult to
> do a first pass.  (And for more learning, Greg Allenby usually teaches a
> tutorial on Bayesian methods including the bayesm package at the AMA's ART
> Forum in June.)
>
> Good luck,
>
> -- Chris
>
>
> -----Original Message-----
> From: r-sig-dcm-bounces@r-project.org [mailto:
> r-sig-dcm-bounces@r-project.org] On Behalf Of Data Analytics Corp.
> Sent: Tuesday, February 01, 2011 5:16 AM
> To: r-sig-dcm@r-project.org
> Subject: Re: [R-sig-DCM] segmenting consumers after a dcm
>
> Good morning,
>
> I've gotten a lot of great responses to my query about the segmentation.  I
> understand the latent class application; I do that for other problems.  The
> question now is how do I estimate a choice model in R using HB?  Bayesm was
> recommended.  I haven't looked at this yet, but will this estimate a logit
> model?  Any examples or applications someone can share?
>
> Thanks,
>
> Walt
>
> ________________________
>
> Walter R. Paczkowski, Ph.D.
> Data Analytics Corp.
> 44 Hamilton Lane
> Plainsboro, NJ 08536
> ________________________
> (V) 609-936-8999
> (F) 609-936-3733
> walt@dataanalyticscorp.com
> www.dataanalyticscorp.com
> _____________________________________________________
> On 2/1/2011 8:09 AM, Shan, Ming (GfK Kynetec US) wrote:
> > Dear Walt,
> >
> >     Nowadays, it is not uncommon at all for people to conduct
> segmentation analysis after DCM.  But one has to run individual respondent
> level estimation (e.g. Hierarchical Bayes mdel) or group level estimation
> (e.g. Latent Class Analysis).  HB is a form of random effect model allowing
> each individual respondent to have varying coefficients but still "pool" the
> information from the others.  You would then run cluster analysis on your
> utilities to group respondents.  In the case of LCA, the segmentation is
> done while estimation is made.
> >
> >    Hope this makes sense and helps somewhat.
> >
> > Kindly,
> > Ming Shan
> >
> > Direct:  +1 (314) 200 4499
> > Mobile: +1 (314) 680 2525
> >
> > www.gfk-kynetec.com
> >
> >
> > -----Original Message-----
> > From: r-sig-dcm-bounces@r-project.org
> > [mailto:r-sig-dcm-bounces@r-project.org] On Behalf Of
> > r-sig-dcm-request@r-project.org
> > Sent: Tuesday, February 01, 2011 5:00 AM
> > To: r-sig-dcm@r-project.org
> > Subject: R-SIG-DCM Digest, Vol 4, Issue 1
> >
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> >     1. segmenting consumers after a dcm (Data Analytics Corp.)
> >
> >
> > ----------------------------------------------------------------------
> >
> > Message: 1
> > Date: Mon, 31 Jan 2011 14:53:26 -0500
> > From: "Data Analytics Corp."<walt@dataanalyticscorp.com>
> > To: r-sig-dcm@r-project.org
> > Subject: [R-sig-DCM] segmenting consumers after a dcm
> > Message-ID:<4D471336.2090009@dataanalyticscorp.com>
> > Content-Type: text/plain; charset=ISO-8859-1; format=flowed
> >
> > Hi,
> >
> > I just completed a dcm (not a conjoint) for a client.  Now they would
> > like to segment the consumers based on utilities from the model,
> > claiming that they had this done for them before although they don't
> > know what or how it was done.  My model had coefficients estimated
> > across all consumers, not for each one separately.  I don't even think
> > that makes sense to do for each individual.  Has anyone ever done
> > segmentation after a dcm?
> >
> > Thanks,
> >
> > Walt
> >
> > ________________________
> >
> > Walter R. Paczkowski, Ph.D.
> > Data Analytics Corp.
> > 44 Hamilton Lane
> > Plainsboro, NJ 08536
> > ________________________
> > (V) 609-936-8999
> > (F) 609-936-3733
> > walt@dataanalyticscorp.com
> > www.dataanalyticscorp.com
> >
> >
> >
> > ------------------------------
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