Hellinger distance based univariate regression for proportions {Rfast2}

Hellinger distance based univariate regression for proportions

Description

Hellinger distance based univariate regression for proportions.

Usage

prophelling.reg(y, x, cov = FALSE, tol = 1e-07, maxiters = 100) 

Arguments

y

The dependent variable, a numerical vector with percentages.

x

A numerical matrix with the indendent variables. We add, internally, the first column of ones.

cov

Should the sandwich covariance matrix and the standard errors be returned? If yes, set this equal to TRUE.

tol

The tolerance value to terminate the Newton-Raphson algorithm.

maxiters

The max number of iterations that can take place in each regression.

Details

We minimise the Jensen-Shannon divergence instead of the ordinarily used divergence, the Kullback-Leibler. Both of them fall under the \phi-divergence class models and hance this one produces asympottically normal regression coefficients as well.

Value

A list including:

be

The regression coefficients.

seb

The sandwich standard errors of the beta coefficients, if the input argument argument was set to TRUE.

covb

The sandwich covariance matrix of the beta coefficients, if the input argument argument was set to TRUE.

js

The final Jensen-Shannon divergence.

H

The final Hellinger distance.

iters

The number of iterations required by Newton-Raphson.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Tsagris, Michail (2015). A novel, divergence based, regression for compositional data. Proceedings of the 28th Panhellenic Statistics Conference, 15-18/4/2015, Athens, Greece. https://arxiv.org/pdf/1511.07600.pdf

See Also

propols.reg, simplex.mle, kumar.mle

Examples

y <- rbeta(150, 3, 4)
x <- iris
a <- prophelling.reg(y, x)