This model is very recently proposed by
. 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.
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.
for a bundle of MATLAB codes, which includes code to estimate
for Bioson Biogeme G-RRM estimation code to estimate shopping
for Python Biogeme G-RRM estimation code to estimate shopping
for Pandas Biogene G-RRM estimation code to estimate shopping
for Apollo R G-RRM estimation code to estimate shopping choice
Example Data File
to download the example shopping choice data file (see
Arentze et al. 2005
for more details on the data)