| mci.shares {MCI} | R Documentation |
MCI market share/market area simulations
Description
This function calculates (local) market shares based on specified explanatory variables and their weighting parameters in a given MCI interaction matrix.
Usage
mci.shares(mcidataset, submarkets, suppliers, ..., mcitrans = "lc", interc = NULL)
Arguments
mcidataset |
an interaction matrix which is a |
submarkets |
the column in the interaction matrix |
suppliers |
the column in the interaction matrix |
... |
the column(s) of the explanatory variable(s) (at least one), numeric and positive (or dummy [1,0]), and their weighting parameter(s). The parameter(s) must follow the particular variable(s): |
mcitrans |
defines if the regular multiplicative formula is used or the inverse log-centering transformation where the explanatory variables are MCI-transformed and linked by addition in an exponential function instead of multiplication. This transformation is necessary if an intercept is included in the model and/or if dummy variables are used as explanatories (default: |
interc |
if |
Details
In this function, the input dataset (MCI interaction matrix) is used for a calculation of (local) market shares (p_{ij}), based on (at least one) given explanatory variable(s) and (a) given weighting parameter(s). If an intercept is included in the model and/or if dummy variables are used as explanatories, the inverse log-centering transformation by Nakanishi/Cooper (1982) has to be used for simulations (mcitrans = "ilc").
Value
The function mci.shares() returns the input interaction matrix (data.frame) with new variables/columns, where the last one (p_ij) is the one of interest, containing the (local) market shares of the j suppliers in the i submarkets (p_{ij}).
Author(s)
Thomas Wieland
References
Huff, D. L./Batsell, R. R. (1975): “Conceptual and Operational Problems with Market Share Models of Consumer Spatial Behavior”. In: Advances in Consumer Research, 2, p. 165-172.
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. (2013): “Einkaufsstaettenwahl, Einzelhandelscluster und raeumliche Versorgungsdisparitaeten - Modellierung von Marktgebieten im Einzelhandel unter Beruecksichtigung von Agglomerationseffekten”. In: Schrenk, M./Popovich, V./Zeile, P./Elisei, P. (eds.): REAL CORP 2013. Planning Times. Proceedings of 18th International Conference on Urban Planning, Regional Development and Information Society. Schwechat. p. 275-284. http://www.corp.at/archive/CORP2013_98.pdf
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, shares.total
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