The G-RRM model

 

This model is very recently proposed by Chorus 2014. This model generalises the RRM2010 model, and has - just as the µRRM model - beside the RRM2010 model also the linear-additive RUM model as a special case.

 

 

In the G-RRM model the fixed constant of one in the attribute level regret function of the RRM2010 model is replaced by a regret-weight variable denoted γ, see the equation below. γ can be estimated for each attribute seperately, or one can estimate the model using one generic γ.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MATLAB

  • Click here for a bundle of MATLAB codes, which includes code to estimate G-RRM-MNL models

 

BISON BIOGEME

  • Click here for BISON BIOGEME G-RRM estimation code to estimate shopping choice data

 

PYTHON BIOGEME

  • Click here for PYTHON BIOGEME G-RRM estimation code to estimate shopping choice data

 

PANDAS BIOGEME

  • Click here for PANDAS BIOGEME G-RRM estimation code to estimate shopping choice data

Apollo R

  • Click here for Apollo R G-RRM estimation code to estimate shopping choice data.

EXAMPLE DATA FILE

  • Click here to download the example shopping choice data file (see Arentze et al. 2005 for more details on the data)

G-RRM model

The figure on the right shows the effect of the size of γ on the attribute level regret function. As can be seen, a γ = 1 results in the Classical RRM model. As γ gradually increases the attribute level regret function becomes less convex. In the special case in which γ = 0, the G-RRM model predicts the same choice behaviour as the linear-additive RUM model.

G-RRM equations