compareModels: compare several models

In promotionImpact: Analysis & Measurement of Promotion Effectiveness

Description

compareModels

Usage

compareModels(
  data,
  promotion,
  fix = list(logged = TRUE, differencing = TRUE),
  time.field = "dt",
  target.field = "sales",
  dummy.field = NULL,
  trend.param = 0.05,
  period.param = 3,
  var.type = "smooth",
  smooth.except.date = NULL,
  smooth.bandwidth = 2,
  smooth.var.sum = TRUE,
  allow.missing = TRUE
)

Arguments

data	Dataframe containing date, target variable, and some additional time dummies that the researcher wants to account for.
promotion	Dataframe containing promotion ID, start date, end date, promotion tag(type). Might include daily payments associated with the promotion.
fix	A List of constraints to find the best model. Constraints can only be in following list: 'period','trend','logged','synergy.var','differencing','smooth.origin','structural.change','synergy.promotion'
time.field	Specify the date field of 'data'.
target.field	Specify the target field of 'data'.
dummy.field	Specify the additional time dummies of 'data'.
trend.param	Flexibility of trend component. Default is 0.05, and as this value becomes larger, the trend component will be more flexible.
period.param	Flexibility of period component. Default is 3, and as this value becomes larger, the period component will be more flexible.
var.type	'smooth' to use smoothed promotion variables, 'dummy' to use dummy promotion variables
smooth.except.date	Date value that will be excluded from the smoothing process. eg) '01' to exclude every start day of a month
smooth.bandwidth	Bandwidth of local polynomial regression used in the smoothing process. Default value is 2.
smooth.var.sum	If TRUE, the smoothing values for times when multiple promotions in a single tag overlap will be the values from the latest promotion. Otherwise, the values will be added(default).
allow.missing	TRUE to allow missing data in promotion sales during the promotion period

Details

compareModels compares several models under user-defined conditions and suggests the best options.

Examples

comparison <- compareModels(data = sim.data, promotion = sim.promotion.sales,
                            fix = list(logged = TRUE, differencing = TRUE, smooth.origin='all',
                                       trend = FALSE, period = NULL), 
                            time.field = 'dt', target.field = 'simulated_sales', 
                            trend.param = 0.02, period.param = 2)
 

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