compute.dc {quarrint}

Discrete Choice Model-based Interaction Index for a Quarry

Description

Given an object of type quarry, the function computes the probabilities of each level of interaction (low, medium, high and very high) using a Logit discrete choice model.

Usage

## S3 method for class 'quarry'
compute.dc(x, ...)

Arguments

x

An object of type quarry.

...

Further arguments passed to or from other methods.

Details

The model parameters have been estimated with BIOGEME and has an adjusted \rho^2 of 0.609. The model is fully detailed in the paper "Interaction prediction between groundwater and quarry extension using discrete choice models and artificial neural networks" (Barthelemy et al., 2016).

Value

A list whose elements are:

p.low

The probability of a low interaction level.

p.medium

The probability of a medium interaction level.

p.high

The probability of a high interaction level.

p.very.high

The probability of a very high interaciton level.

Note

In order to use the function, the quarry must have been constructed with the function quarry using the default parameters.

Author(s)

Johan Barthelemy.

Maintainer: Johan Barthelemy [e-mail].

References

Barthelemy, J., Carletti, T., Collier L., Hallet, V., Moriame, M., Sartenaer, A. (2016) Interaction prediction between groundwater and quarry extension using discrete choice models and artificial neural networks Environmental Earth Sciences (in press)

Collier, L., Barthelemy, J., Carletti, T., Moriame, M., Sartenaer, A., Hallet, V. (2015) Calculation of an Interaction Index between the Extractive Activity and Groundwater Resources Energy Procedia 76, 412-420

Bierlaire, M. (2003) BIOGEME: a free package for the estimation of discrete choice models. Swiss Transport Research Conference TRANSP-OR-CONF-2006-048

See Also

compute.ann to compute an interaction index based on an artificial neural network and compute.interaction to predict the interaction between between the quarry and the groundwater using both the discrete choice-based index and the neural network-based index.

Examples

# creating a quarry
q <- quarry(geological.context = 2, hydrogeological.context = 4,
            piezometric.context = 1, quarry.position = 4,
            production.catchment = 4, quality.catchment = 3)

# computing the interaction index
inter.idx <- compute.dc(q)