MCI (version 1.0.1)

mci.transvar: Log-centering transformation of one variable in an interaction matrix

Description

This function applies the log-centering transformation on a variable in a given MCI interaction matrix.

Usage

mci.transvar (mcidataset, submarkets, suppliers, mcivariable, 
output_ij = FALSE, output_var = "numeric")

Arguments

mcidataset

an interaction matrix which is a data.frame containing the submarkets, suppliers and the regarded variables (e.g. the observed market shares, $p_{ij}$, and the explanatory variables)

submarkets

the column in the interaction matrix mcidataset containing the submarkets, should usually be a factor

suppliers

the column in the interaction matrix mcidataset containing the suppliers, should usually be a factor

mcivariable

the column of the variable to be transformed, numeric and positive (or dummy [1,0])

output_ij

boolean argument that indicates if the function output has to be a data.frame with three columns (submarkets, suppliers, transformed variable) or a vector only with the transformed values (default is output_ij = FALSE)

output_var

defines the mode of the function output if output_ij = FALSE (default is output_var = "numeric", otherwise "list")

Value

• The format of the output can be controlled by the last two arguments of the function (see above). Either a new data.frame with the transformed input variable and the submarkets/suppliers or a vector with the transformed values only. The name of the input variable is passed to the new data.frame marked with a "_t" to indicate that it was transformed (e.g. "shares_t" is the transformation of "shares").

Details

The regarded variable in the input dataset is transformed to regression-ready data with the log-centering transformation by Nakanishi/Cooper (1974) (to transform a complete interaction matrix, use mci.transmat(), for transformation and fitting use mci.fit()). The log-centering transformation can be regarded as the key concept of the MCI model because it enables the model to be estimated by OLS (ordinary least squares) regression. The function identifies dummy variables which are not transformend (because they do not have to be).

References

Huff, D. L./McCallum, D. (2008): Calibrating the Huff Model Using ArcGIS Business Analyst. ESRI White Paper, September 2008. 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. 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

Examples

data(ce)
# Loads the data

mci.transvar (ce, "origin_code", "store_code", "ms_obs", output_ij=TRUE)
# Output: submarkets (origins), store codes and transformations of "ms_obs"

mci.transvar (ce, "origin_code", "store_code", "ms_obs")
# Output: a numeric vector containing the transformated values of "ms_obs"

transf_mcivar <- mci.transvar (ce, "origin_code", "store_code", "ms_obs", output_ij=TRUE)
# Save in a new data frame called "transf_mcivar"