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Fast Estimation of Multinomial Logit Models: R Package mnlogit

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  • Hasan, Asad
  • Wang, Zhiyu
  • Mahani, Alireza S.

Abstract

We present the R package mnlogit for estimating multinomial logistic regression models, particularly those involving a large number of categories and variables. Compared to existing software, mnlogit offers speedups of 10 - 50 times for modestly sized problems and more than 100 times for larger problems. Running in parallel mode on a multicore machine gives up to 4 times additional speedup on 8 processor cores. mnlogit achieves its computational efficiency by drastically speeding up computation of the log-likelihood function's Hessian matrix through exploiting structure in matrices that arise in intermediate calculations. This efficient exploitation of intermediate data structures allows mnlogit to utilize system memory much more efficiently, such that for most applications mnlogit requires less memory than comparable software by a factor that is proportional to the number of model categories.

Suggested Citation

  • Hasan, Asad & Wang, Zhiyu & Mahani, Alireza S., 2016. "Fast Estimation of Multinomial Logit Models: R Package mnlogit," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i03).
    • Handle: RePEc:jss:jstsof:v:075:i03
    DOI: http://hdl.handle.net/10.18637/jss.v075.i03
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    References listed on IDEAS

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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
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    Cited by:

    1. Yves Croissant, 2020. "mlogit: Random Utility Models in R," Post-Print hal-03019603, HAL.
    2. Sarrias, Mauricio & Daziano, Ricardo, 2017. "Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i02).
    3. Llorca, Carlos & Ji, Joanna & Molloy, Joseph & Moeckel, Rolf, 2018. "The usage of location based big data and trip planning services for the estimation of a long-distance travel demand model. Predicting the impacts of a new high speed rail corridor," Research in Transportation Economics, Elsevier, vol. 72(C), pages 27-36.
    4. Benjamin Quost & Thierry Denœux & Shoumei Li, 2017. "Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 659-690, December.

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