Compositional (version 1.1)

kl.compreg: Kullback-Leibler regression

Description

Regression based on the Kullback-Leibler divergence.

Usage

kl.compreg(y, x, B = 1000, ncores = 4, xnew = NULL)

Arguments

y

A matrix with the compositional data (dependent variable). Zero values are allowed.

x

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

B

If B is greater than 1 bootstrap estimates of the standard error are returned. If B=1, no standard errors are returned.

ncores

If ncores is 2 or more parallel computing is performed. This is to be used for the case of bootstrap. If B=1, this is not taken into consideration.

xnew

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

Value

• A list including:
• beta
  The beta coefficients.
• seb
  The standard error of the beta coefficients, if bootstrap is chosen, i.e. if B > 1.
• est
  The fitted or the predicted values (if xnew is not NULL).

Details

The Kullback-Leibler divergence is adopted as the objective function. This involves numerical optimisation. There is no log-likelihood.

References

Murteira, Jose MR, and Joaquim JS Ramalho. Regression analysis of multivariate fractional data. Econometric Reviews (To appear).

See Also

diri.reg, esov.compreg, ols.compreg, comp.reg

Examples

library(MASS)
x <- fgl[, 1]
y <- fgl[, 2:9]
mod1<- kl.compreg(y, x, B = 1, ncores = 1)
mod2 <- esov.compreg(y, x, B = 1, ncores = 1)
mod1
mod2