ijmatrix.shares: Market shares in interaction matrix
In MCI: Multiplicative Competitive Interaction (MCI) Model

Description Usage Arguments Details Value Author(s) References See Also Examples

Calculating market shares in an interaction matrix based on the observations of the regarded variable.

1 2	ijmatrix.shares(rawmatrix, submarkets, suppliers, observations, varname_total = "freq_i_total", varname_shares = "p_ij_obs")

`rawmatrix`	a `data.frame` containing the submarkets, suppliers and the observed data
`submarkets`	the column in the dataset containing the submarkets (e.g. ZIP codes)
`suppliers`	the column in the dataset containing the suppliers (e.g. store codes)
`observations`	the column with the regarded variable (e.g. frequencies, expenditures, turnovers)
`varname_total`	character value, name of the variable for the total absolute values of the i submarkets in the output (default: `varname_total = "freq_i_total"`)
`varname_shares`	character value, name of the variable for the market shares p_{ij} in the output (default: `varname_shares = "p_ij_obs"`)

This function calculates the market shares of every j in every i (p_{ij}) based on an existing interaction matrix.

The input interaction matrix which is a data.frame with a new column 'p_ij_obs' (or another stated name in the argument varname_shares) or, if used after ijmatrix.create, an update of the columns 'freq_i_total' and 'p_ij_obs' (or different stated names in the arguments varname_total and/or varname_shares).

Thomas Wieland

Cooper, L. G./Nakanishi, M. (2010): “Market-Share Analysis: Evaluating competitive marketing effectiveness”. Boston, Dordrecht, London : Kluwer (first published 1988). E-book version from 2010: http://www.anderson.ucla.edu/faculty/lee.cooper/MCI_Book/BOOKI2010.pdf

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

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.

ijmatrix.create

data(grocery1)
# Loads the data

mymcidata <- ijmatrix.create (grocery1, "plz_submarket", "store_code")
# Creates an interaction matrix with market shares based on the frequencies 
# of visited grocery stores and saves results directly in a new dataset
mymcidata$freq_ij_corr <- var.correct(mymcidata$freq_ij_abs, 1)
# Corrects the frequency variable (no zero or negative values allowed)
mymcidata_shares <- ijmatrix.shares(mymcidata, "plz_submarket", "store_code", "freq_ij_corr")
# Calculates market shares based on the corrected frequencies
# and saves the results as a new dataset