model.fit: Goodness of fit statistics for the Huff model

In MCI: Multiplicative Competitive Interaction (MCI) Model

Description

This function calculates several goodness of fit values to evaluate how good the observed values fit the empirical observations.

Usage

model.fit(y_obs, y_exp, plotVal = FALSE)

Arguments

y_obs	Observed values of the dependent variable y
y_exp	Expected values of the dependent variable y (model results)
plotVal	Logical argument that indicates if the function plots a graph comparing observed and expected values

Details

This function computes several goodness of fit statistics to evaluate the results of non-linear fitting procedures for the Huff model (see the functions huff.attrac and huff.fit). Besides the sum of squared residuals, the function also calculates a Pseudo-R-squared measure and the MAPE (mean average percentage error), both used by De Beule et al. (2014), and the global error used by Klein (1988).

Value

list:

resids_sq_sum	Sum of squared residuals
pseudorsq	Pseudo-R-squared
globerr	Global error
mape	Mean average percentage error

Author(s)

Thomas Wieland

References

De Beule, M./Van den Poel, D./Van de Weghe, N. (2014): “An extended Huff-model for robustly benchmarking and predicting retail network performance”. In: Applied Geography, 46, 1, p. 80-89.

Klein, R. (1988): “Der Lebensmittel-Einzelhandel im Raum Verden. Raeumliches Einkaufsverhalten unter sich wandelnden Bedingungen”. Flensburger Arbeitspapiere zur Landeskunde und Raumordnung, 6. Flensburg.

See Also

huff.fit

Examples

# Controlling the fit of a Huff Model market area estimation #

data(Freiburg1)
data(Freiburg2)
data(Freiburg3)
# Loads the data

huff_mat <- huff.shares (Freiburg1, "district", "store", "salesarea", "distance")
# Market area estimation using the Huff Model with standard parameters
# (gamma = 1, lambda = -2)

huff_mat_pp <- merge (huff_mat, Freiburg2)
# Adding the purchasing power data for the city districts

huff_total <- shares.total (huff_mat_pp, "district", "store", "p_ij", "ppower")
# Total expected sales and shares

huff_total_control <- merge (huff_total, Freiburg3, by.x = "suppliers_single",
by.y = "store")

model.fit(huff_total_control$annualsales, huff_total_control$sum_E_j, plotVal = TRUE)
# Observed vs. expected

# Results can be adressed directly:
huff_fit <- model.fit(huff_total_control$annualsales, huff_total_control$sum_E_j, plotVal = TRUE)
huff_fit$mape

Example output

$resids_sq_sum
[1] 2.125162e+15

$pseudorsq
[1] 0.5128422

$globerr
[1] 0.5210329

$mape
[1] 0.6383766

[1] 0.6383766

MCI documentation built on May 2, 2019, 6:02 a.m.