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Model Fit & Concepts of Interaction

Review of Logistic and Proportional Hazards Regression Model Selection:

You can look at changes in the deviance (-2 log likelihood change)
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  • Deviance - Residual sum of squares with normal data
    • Problem: Deviances alone do not penalize "model complexity" (hence need LRT - but onlu applies to nested models)
  • AIC, BIC - More commonly used
    • Larger BIC and AIC -> worse model
    • BIC is more conservative
    • Both based on likelihood
    • An advantage is that you do not need hierarchical models to compare the AIC or BIC between models
    • A disadvantage is there is no test nor p-value that goes with comparison of models
    • A model with smaller values of AIC or BIC provides a better fit
    • Used to compare non-nested models
      • A non-nested model refers to one that is not nested in another; the set of independent variables in one model is not a subset of the independent variables in the other models
      • The data must be the same

 

 

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