Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Asad Hasan, Zhiyu Wang, Alireza S. Mahani
Title: Fast Estimation of Multinomial Logit Models: R Package mnlogit
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.

Page views:: 4157. Submitted: 2014-04-04. Published: 2016-11-19.
Paper: Fast Estimation of Multinomial Logit Models: R Package mnlogit     Download PDF (Downloads: 5844)
mnlogit_1.2.5.tar.gz: R source package Download (Downloads: 212; 308KB)
v75i03.R: R replication code Download (Downloads: 269; 20KB)
simChoiceModel.R: R source code Download (Downloads: 263; 2KB)

DOI: 10.18637/jss.v075.i03

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.