Eliminating predictors
You technically still do not know which predictors are really required in the model. Again, you use the function stepAIC() from the add-on package MASS to exclude unnecessary predictors. The argument direction = "backward" starts the selection process with the extended.model and sequentially removes terms in an effort to lower the AIC. The argument trace = FALSE suppresses information to be printed during the running of the selection process. You summarize the final model, resulting in the minimum AIC value, by the function summary().
This exercise is part of the course
Building Response Models in R
Exercise instructions
- Perform backward selection of predictors on the
extended.modelobject by using the functionstepAIC(). Assign the result to an object namedfinal.model. - Summarize the
final.modelobject by using the functionsummary().
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Backward elemination
final.model <- ___(___, direction = ___, trace = ___)
# Summarize the final.model