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.model
object by using the functionstepAIC()
. Assign the result to an object namedfinal.model
. - Summarize the
final.model
object 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