shares.total {MCI} | R Documentation |
Total market shares/market areas
Description
This function calculates the total sales and market shares (or total market area) of the suppliers based on a given interaction matrix which already contains (local) market shares.
Usage
shares.total(mcidataset, submarkets, suppliers, shares, localmarket,
plotChart = FALSE, plotChart.title = "Total sales", plotChart.unit = "sales",
check_df = TRUE)
Arguments
mcidataset |
an interaction matrix which is a |
submarkets |
the column in the interaction matrix |
suppliers |
the column in the interaction matrix |
shares |
the column in the interaction matrix |
localmarket |
the column in the interaction matrix |
plotChart |
logical argument that indicates if the total values shall be visualized in a bar plot (default: |
plotChart.title |
If |
plotChart.unit |
If |
check_df |
logical argument that indicates if the input (dataset, column names) is checked (default: |
Details
If (local) market shares are observed and estimated, respectively, it is possible to link them to a (local) market potential to estimate the total sales and shares of the given suppliers. In this function, the input dataset (interaction matrix with local market shares) is used for the calculation of total sales (or total number of customers) and total market shares of all j
regarded suppliers. Optionally, the function also returns a simple bar plot of the total values.
Value
Returns a new data.frame
with the total sales (sum_E_j
) and the over-all market shares of the j
suppliers (share_j
).
Author(s)
Thomas Wieland
References
Huff, D. L./McCallum, D. (2008): “Calibrating the Huff Model Using ArcGIS Business Analyst”. ESRI White Paper, September 2008. https://www.esri.com/library/whitepapers/pdfs/calibrating-huff-model.pdf
Nakanishi, M./Cooper, L. G. (1974): “Parameter Estimation for a Multiplicative Competitive Interaction Model - Least Squares Approach”. In: Journal of Marketing Research, 11, 3, p. 303-311.
Nakanishi, M./Cooper, L. G. (1982): “Simplified Estimation Procedures for MCI Models”. In: Marketing Science, 1, 3, p. 314-322.
Wieland, T. (2015): “Raeumliches Einkaufsverhalten und Standortpolitik im Einzelhandel unter Beruecksichtigung von Agglomerationseffekten. Theoretische Erklaerungsansaetze, modellanalytische Zugaenge und eine empirisch-oekonometrische Marktgebietsanalyse anhand eines Fallbeispiels aus dem laendlichen Raum Ostwestfalens/Suedniedersachsens”. Geographische Handelsforschung, 23. 289 pages. Mannheim : MetaGIS.
See Also
mci.fit
, mci.transmat
, mci.transvar
, mci.shares
Examples
data(Freiburg1)
data(Freiburg2)
# Loads the data
mynewmatrix <- mci.shares(Freiburg1, "district", "store", "salesarea", 1, "distance", -2)
# Calculating shares based on two attractivity/utility variables
mynewmatrix_alldata <- merge(mynewmatrix, Freiburg2)
# Merge interaction matrix with district data (purchasing power)
shares.total (mynewmatrix_alldata, "district", "store", "p_ij", "ppower")
# Calculation of total sales