Latent Class Model

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Gamal Eldeeb

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Oct 3, 2018, 12:29:05 PM10/3/18
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Hi Michel, 

I am wondering if there is any documentation to help developing a latent class model using PythonBiogeme.

Thanks,
Gamal

Michel Bierlaire

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Oct 3, 2018, 12:37:02 PM10/3/18
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Alireza Rahimi

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Jun 20, 2019, 7:27:23 PM6/20/19
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Dear Professor Bierlaire,

I am trying to develop a hybrid choice model to simultaneously estimate the choice and attitudinal variables. In addition, similar to this paper, I want to include the latent class model (LCM) in the main model. Unfortunately, I did not found any sample syntax for LCM and the above link that you provided is not working anymore.
I would be more than grateful if you could please give me an example of the syntax or some description for the coding of a hybrid choice model with LCM?

Best regards,
Ali

On Wednesday, October 3, 2018 at 12:37:02 PM UTC-4, Michel Bierlaire wrote:
On 3 Oct 2018, at 17:54, Gamal Eldeeb <gamal...@gmail.com> wrote:

Hi Michel, 

I am wondering if there is any documentation to help developing a latent class model using PythonBiogeme.

Thanks,
Gamal

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Michel Bierlaire

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Jun 21, 2019, 4:24:26 AM6/21/19
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For pandasbiogeme, the examples for latent variables are here http://biogeme.epfl.ch/examples_latent.html


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Alireza Rahimi

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Jun 29, 2019, 11:33:30 PM6/29/19
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Professor Bierlaire,

Thank you for your response to my inquiry. I appreciate the information you have provided.

The previous latent class script provides us only with the probabilities associated with each latent class. However, we are looking for an approach to model class membership probabilities using a separate multinomial logit model. That is, the final outcomes of the syntax should be two different logit models: one for class membership, and the other for the choice model being analyzed in each class. Is the Biogeme package capable of doing this? and if yes, I would be more than grateful if you can give us a reference or a simple example?

Best regards,
Ali

Michel Bierlaire

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Jun 30, 2019, 7:55:57 PM6/30/19
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Just replace the probability by a model. 

R = beta1 * age + beta2 * income + …
probabilityClass1 = 1 / (1 + exp(R))
probabilityClass2 = 1 -probabilityClass1 


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