Usually, either adjusted R-squared or Mallows’ Cp is the criterion for picking the best fitting models for this process. Additionally, if you use one of these procedures, you should consider it as only the first step of the model selection process.Īfter fitting all of the models, best subsets regression then displays the best fitting models with one independent variable, two variables, three variables, and so on. However, if theory and expertise are strong guides, it’s generally better to follow them than to use an automated procedure. These procedures are especially useful when theory and experience provide only a vague sense of which variables you should include in the model. You could specify many models with different combinations of independent variables, or you can have your statistical software do this for you. These automatic procedures can be helpful when you have many independent variables and you need some help in the investigative stages of the variable selection process. In this post, I compare how these methods work and which one provides better results. Stepwise regression and Best Subsets regression are two of the more common variable selection methods. Automatic variable selection procedures are algorithms that pick the variables to include in your regression model.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |