*! randregret 1.1.0 24Dec2020
*! author aagv
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V.1.0: d0 ml evaluator that run the following RRM models:
->RRM (Chorus, 2010)
->muRRM (S. Van Cranenburgh et.al, 2015)
->pure-RRM (S. Van Cranenburgh et.al, 2015)
->G-RRM (Chorus, 2014)
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Here we describe the randregret command (published at The Stata Journal), which implements a variety of Random Regret Minimization (RRM) models. The command allows the user to apply the classic RRM model introduced in Chorus (2010), the Generalized RRM model introduced in Chorus (2014), and also the muRRM and Pure RRM models, both introduced in van Cranenburgh (2015). We illustrate the usage of the randregret command using stated choice data on route preferences using data from van Cranenburgh (2018). The command offers robust and cluster standard error correction using analytical expressions of the scores functions. It also offers likelihood ratio tests that can be used to assess the relevance of a given model specification. Finally, users can obtain the predicted probabilities from each model using the randregretpred command.
keywords: randregret, randregret_pure, randregretpred, discrete choice models, semi-compensatory behavior, random utility maximization, random regret minimization.
*Describe randregret
net describe randregret, from("https://raw.githubusercontent.com/alvarogutyerrez/randregret/master/src/")
*Install randregret
cap ado uninstall randregret
net install randregret, from("https://raw.githubusercontent.com/alvarogutyerrez/randregret/master/src/")
/*---- PREAMBLE ----*/
clear all
set more off
capture log close
// randregret instalation
cap ado uninstall randregret
net install randregret, from("https://raw.githubusercontent.com/alvarogutyerrez/randregret/master/src/")
// Data download
scalar server = "https://data.4tu.nl/ndownloader/"
scalar doi = "files/24015353"
import delimited "`=server + doi'" ,clear
keep obs id cs tt1 tc1 tt2 tc2 tt3 tc3 choice
list obs id cs tt1 tc1 tt2 tc2 tt3 tc3 choice in 1/4,sepby(obs)
// Data processing
rename (choice) (choice_w)
reshape long tt tc , i(obs) j(altern)
generate choice = 0
replace choice = 1 if choice_w==altern
label define alt_label 1 "First" 2 "Second" 3 "Third"
label values altern alt_label
list obs altern choice id cs tt tc in 1/12, sepby(obs)
// Different Regret Models:
// Classic RRM+ cluster(id)
randregret choice tc tt , gr(obs) alt(altern) rrmfn(classic) cluster(id) nocons
// muRRM + cluster(id)
randregret choice tc tt , gr(obs) alt(altern) rrmfn(mu) cluster(id) show nocons
// Generalized RRM + cluster(id)
randregret choice tc tt , gr(obs) alt(altern) rrmfn(gene) cluster(id) show nocons
// Pure RRM + cluster(id)
randregret choice , neg(tc tt) gr(obs) alt(altern) rrmfn(pure) cluster(id) nocons
The article that documents the command can be found here!
- Stata Meeting 2020, London, UK. Slides available here.
- Stata Meeting 2020, Bern, Switzerland. Slides are coming soon.
Professor Sander van Cranenburgh has a website where you can find additional information about RRM models together with routines in other languages to fit them (R, Python, and Matlab included). A dofile that replicated professor Sander's results can be found here.
- Chorus. C. 2010. A New Model of Random Regret Minimization. European Journal of Transport and Infrastructure Research 10: pp. 181-196.
- Chorus. C. 2013. A Generalized Random Regret Minimization model. Transportation Research Part B: Methodological 68: pp. 224-238.
- Van Cranenburgh S., C.A. Guevara and C.G. Chorus 2015. New insights on random regret minimization models. Transportation Research Part A: Policy and Practice 74: pp. 91-109.
- Van Cranenburgh S. and Alwosheel A. 2019. An artificial neural network based approach to investigate travellers’ decision rules. Transportation Research Part C: Emerging Technologies 98: pp. 152-166.
- Van Cranenburgh, S. 2018. Small value-of-time experiment, Netherlands. 4TU.Centre for Research Data. Dataset.