MCI (version 1.2.0)

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

Description

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

Usage

model.fit(y_obs, y_exp)

Arguments

y_obs

Observed values of the dependent variable $y$

y_exp

Expected values of the dependent variable $y$ (model results)

Value

list: list:

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).

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

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

# NOTE: This may take a while!
# huffmat_total <- huff.attrac(Freiburg1, "district", "store", "salesarea", "distance", lambda = -2,
# dtype= "pow", lambda2 = NULL, Freiburg2, "ppower", Freiburg3, "store", "annualsales", 
# output = "total", show_proc = TRUE)
# Local optimization of store attractivity using the function huff.attrac()
# returns a data frame with total values (observed and expected after optimization)
# which is stored into huffmat_total

# model.fit(huffmat_total$total_obs, huffmat_total$sum_E_j)
# returns a list with fit statistics (sum of sq. resid., pseudo-R-squared, global error, mape)

# Results can be adressed directly:
# huff_fit <- model.fit(huffmat_total$total_obs, huffmat_total$sum_E_j)
# huff_fit$mape