Eliminating predictors
You do not want to exclude the lagged coupon effect yet. The company needs a really sound model to improve it's future marketing decisions and to effectively run their campaign. To be 100% sure, you perform backward selection of predictors by using the function stepAIC()
, loaded from the add-on package MASS
.
The stepAIC()
function builds all possible combinations of predictors and determines which has the lowest AIC. 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. The final model, resulting in the minimum AIC value, is summarized by the function summary()
.
This exercise is part of the course
Building Response Models in R
Exercise instructions
- Load the add-on package
MASS
by using the functionlibrary()
. - Perform backward elimination 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.
# Load the MASS package
# Backward elemination
final.model <- ___(___, direction = ___, trace = ___)
# Summarize the final model