Skip to main content

Table 1 Comparison of variable selection methods

From: The value of Bayesian predictive projection for variable selection: an example of selecting lifestyle predictors of young adult well-being

Model R2 RMSE # of selected predictors Selected predictors
Reference model 0.331 0.858 (28)  
Freq. multiple regression 0.332 0.858 (28)  
Projected submodel (1 SE) 0.253 0.883 3 Felt refreshed after waking up today, had trouble concentrating today, servings of fruit today
Projected submodel (matched) 0.284 0.864 6 Felt refreshed after waking up today, had trouble concentrating today, servings of fruit today, servings of soft drink last night, servings of vegetables today, gender: female
Stepwise selection (AIC) 0.315 0.872 10 Felt refreshed after waking up today, ethnicity: asian, had trouble concentrating today, gender: female, servings of soft drink last night, servings of sweets today, servings of sweets last night, felt tired today, servings of fruit today, bmi
Stepwise selection (p-values) 0.275 0.871 8 Felt refreshed after waking up today, had trouble concentrating today, gender: female, servings of sweets today, felt tired today, servings of sweets last night, servings of fruit today, servings of soft drink last night
LASSO (1 SE) 0.139 0.897 4 Felt refreshed after waking up today, had trouble concentrating today, servings of fruit today, servings of soft drink last night
LASSO (min.) 0.283 0.857 23
  1. Summary statistics of model selection strategies, showing test data RMSE and Bayesian R2, number of selected predictors, and the names of the significant predictors (where 10 or fewer predictors were selected, ranked by absolute slope size)