@article{ATM9706,
author = {Zhongheng Zhang},
title = {Variable selection with stepwise and best subset approaches},
journal = {Annals of Translational Medicine},
volume = {4},
number = {7},
year = {2016},
keywords = {},
abstract = {While purposeful selection is performed partly by software and partly by hand, the stepwise and best subset approaches are automatically performed by software. Two R functions stepAIC() and bestglm() are well designed for stepwise and best subset regression, respectively. The stepAIC() function begins with a full or null model, and methods for stepwise regression can be specified in the direction argument with character values “forward”, “backward” and “both”. The bestglm() function begins with a data frame containing explanatory variables and response variables. The response variable should be in the last column. Varieties of goodness-of-fit criteria can be specified in the IC argument. The Bayesian information criterion (BIC) usually results in more parsimonious model than the Akaike information criterion.},
issn = {2305-5847}, url = {https://atm.amegroups.org/article/view/9706}
}