detectOutliers: detect some outliers

In promotionImpact: Analysis & Measurement of Promotion Effectiveness

Description

detectOutliers

Usage

detectOutliers(
  model,
  threshold = list(cooks.distance = 1, dfbetas = 1, dffits = 2),
  option = 2
)

Arguments

model	Execution result object : promotionImpact
threshold	List of threshold values to be determined as outliers if greater than the written values
option	The number of indicators that must be greater than the threshold values to be outliers.

Details

detectOutliers extracts outliers which affect the average effects of promotions.

Examples

pri1 <- promotionImpact(data=sim.data, promotion=sim.promotion, 
                       time.field = 'dt', target.field = 'simulated_sales', 
                       trend = FALSE, period = NULL, structural.change = FALSE,
                       logged = TRUE, differencing = TRUE, synergy.promotion = FALSE,
                       synergy.var = NULL, allow.missing = TRUE)
out <- detectOutliers(model = pri1, 
                      threshold = list(cooks.distance=1, dfbetas=1, dffits=2), option = 1)

promotionImpact documentation built on April 13, 2021, 5:06 p.m.