roll_up_optimal_pairs: Roll up the suggested test/control optimal pairs for pseudo...

In MarketMatching: Market Matching and Causal Impact Inference

Description

roll_up_optimal_pairs Takes the suggested optimal pairs from best_matches() and aggregates the data for pseudo power analysis (test_fake_lift()). You run this function and then pass the result (a matched markets object) to test_fake_lift.

Usage

roll_up_optimal_pairs(matched_markets=NULL,
                      percent_cutoff=1,
                      synthetic=FALSE)

Arguments

matched_markets	A matched market object from best_matches.
percent_cutoff	The percent of data (by volume) to be included in the future study. Default is 1. 0.5 would be 50 percent.
synthetic	If set to TRUE, the control markets are not aggregated so BSTS can determine weights for each market and create a synthetic control. If set to FALSE then the control markets are aggregated and each market will essentially get the same weight. If you have many control markets (say, more than 25) it is recommended to choose FALSE. Default is FALSE.

Value

Returns an object of type market_matching. The object has the following elements:

BestMatches	A data.frame that contains the best matches for each market in the input dataset
Data	The raw data used to do the matching
MarketID	The name of the market identifier
MatchingMetric	The name of the matching variable
DateVariable	The name of the date variable
SuggestedTestControlSplits	Always NULL

Examples

## Not run: 
##-----------------------------------------------------------------------
## Generate the suggested test/control pairs
##-----------------------------------------------------------------------
library(MarketMatching)
data(weather, package="MarketMatching")
mm <- best_matches(data=weather, 
                   id="Area",
                   date_variable="Date",
                   matching_variable="Mean_TemperatureF",
                   parallel=FALSE,
                   suggest_market_splits=TRUE,
                   start_match_period="2014-01-01",
                   end_match_period="2014-10-01")
                   
##-----------------------------------------------------------------------
## Roll up the pairs to generate test and control markets
## Synthetic=FALSE means that the control markets will be aggregated 
## -- i.e., equal weighhs in CausalImpact
##-----------------------------------------------------------------------
                   
rollup <- roll_up_optimal_pairs(matched_markets=mm, 
                                percent_cutoff=1, 
                                synthetic=FALSE)
                                
##-----------------------------------------------------------------------
## Pseudo power analysis (fake lift analysis)
##-----------------------------------------------------------------------

results <- test_fake_lift(matched_markets=rollup,
                     test_market="TEST",
                     lift_pattern_type="constant",
                     end_fake_post_period="2015-12-15",
                     prior_level_sd=0.002, 
                     max_fake_lift=0.1)

## End(Not run)

MarketMatching documentation built on Jan. 13, 2021, 5:46 p.m.