Fast Estimation of Multinomial Logit Models: R Package mnlogit

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Asad Zadeh Hasan, Wang Zhiyu, Alireza S. Mahani
Journal/Conference Name Journal of Statistical Software
Paper Category
Paper 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.
Date of publication 2014
Code Programming Language R

Copyright Researcher 2022