Journal
ANNALS OF TRANSLATIONAL MEDICINE
Volume 4, Issue 7, Pages -Publisher
AME PUBLISHING COMPANY
DOI: 10.21037/atm.2016.03.35
Keywords
Logistic regression; interaction; R; best subset; stepwise; Bayesian information criterion
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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.
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