%0 Journal Article %T Variable selection with stepwise and best subset approaches %A Zhang, Zhongheng %J Annals of Translational Medicine %D 2016 %B 2016 %9 %! Variable selection with stepwise and best subset approaches %K %X 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. %U https://atm.amegroups.org/article/view/9706 %V 4 %N 7 %P 136 %@ 2305-5847