Variable selection with stepwise and best subset approaches
Zhejiang University · Jinhua Central Hospital
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…
Citation impact
- FWCI
- 33.94
- Percentile
- 100%
- References
- 11
Authors
1Topics & keywords
- Akaike information criterion
- Bayesian information criterion
- Stepwise regression
- Goodness of fit
- Model selection
- Feature selection
- Mathematics
- Selection (genetic algorithm)