More lags
It might be interesting to learn if coupon actions only cause an immediate effect on sales or if the effect continues into the next period.
Again, you shift the COUPON
predictor back by using the function lag()
. The result is assigned to a new variable Coupon.lag
. Both, the original COUPON
and the Coupon.lag
variables, are added to the log(SALES) ~ PRICE + Price.lag
relationship by using the update()
function.
This exercise is part of the course
Building Response Models in R
Exercise instructions
- Create a lagged variable for
COUPON
namedCoupon.lag
. - Update the
lag.model
object forCOUPON
andCoupon.lag
by using the functionupdate()
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Take the lag of COUPON
Coupon.lag <- ___
# Update the model for COUPON and Coupon.lag
___(lag.model, . ~ . + ___ + ___)