diri.reg: Dirichlet regression

Description

Dirichlet regression

Usage

diri.reg(y, x, plot = TRUE, xnew = NULL)

Arguments

A matrix with the compositional data (dependent variable). Zero values are not allowed.

The predictor variable(s), they have to be continuous.

plot

A boolean variable specifying whether to plot the leverage values of the observations or not. This is taken into account only when xnew = NULL.

xnew

If you have new data use it, otherwise leave it NULL.

Value

A list including:
loglikThe value of the log-likelihood.
phiThe precision parameter.
log.phiThe logarithm of the precision parameter.
std.logphiThe standard error of the logarithm of the precision parameter.
betaThe beta coefficients.
sebThe standard error of the beta coefficients.
levThe leverage values.
estThe fitted or the predicted values (if xnew is not NULL).

Details

A Dirichlet distribution is assumed for the regression. This involves numerical optimisation.

References

Maier, Marco J. (2014) DirichletReg: Dirichlet Regression for Compositional Data in R. Research Report Series/Department of Statistics and Mathematics, 125. WU Vienna University of Economics and Business, Vienna. http://epub.wu.ac.at/4077/1/Report125.pdf Gueorguieva, Ralitza, Robert Rosenheck, and Daniel Zelterman (2008). Dirichlet component regression and its applications to psychiatric data. Computational statistics & data analysis 52(12): 5344-5355.

Examples

Run this code

x <- iris[, 4]
y <- iris[, 1:3]
mod1 <- diri.reg(y, x)
mod2 <- comp.reg(y, x)
mod1
mod2

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