CompositionalSR (version 1.0)

Marginal effects for the alpha-regression model: Marginal effects for the \(\alpha\)-regression model

Description

Marginal effects for the \(\alpha\)-regression model.

Usage

me.ar(be, mu, x, cov_be = NULL)

Value

A list including:

me

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

ame

The average 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.

cov_be

The covariance matrix of the beta regression coefficients. If you pass this argument, then the standard error of the average marginal effects will be returned.

Author

Michail Tsagris.

R implementation and documentation: Michail Tsagris .

Details

The \(\alpha\)-transformation is applied to the compositional data first and then the \(\alpha\)-regression model is applied.

References

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

me.aslx, me.gwar, alfa.reg

Examples

data(fadn)
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- alfa.reg(y, x, 0.2, xnew = x)
me <- me.ar(mod$be, mod$est, x)