CompositionalSR (version 1.0)

Marginal effects for the GWalphaR model: Marginal effects for the GW\(\alpha\)R model

Description

Marginal effects for the GW\(\alpha\)R model.

Usage

me.gwar(be, mu, x)

Value

A list including:

me

An array with the location-specific marginal effects of each component for each predictor variable.

ame

The average location-specific marginal effects of each component for each predictor variable.

Arguments

be

A matrix with the beta regression coefficients of the \(\alpha\)-regression model.

mu

The fitted values of the \(\alpha\)-regression.

x

A matrix with the continuous predictor variables or a data frame. Categorical predictor variables are not suited here.

Author

Michail Tsagris.

R implementation and documentation: Michail Tsagris .

Details

The location-specific marginal effects for the GW\(\alpha\)R model are computed.

References

Tsagris M. (2025). The \(\alpha\)--regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models. https://arxiv.org/pdf/2510.12663

Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf

See Also

gwar, me.aslx, me.ar

Examples

data(fadn)
coords <- fadn[, 1:2]
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- gwar(y, x, a = 1, coords, h = 0.001)
me <- me.gwar(mod$be, mod$est, x)