Compositional (version 1.5)

Non linear least squares regression for compositional data: Non linear least squares regression for compositional data

Description

Non linear least squares regression for compositional data.

Usage

ols.compreg(y, x, B = 1, ncores = 1, 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:

runtime

The time required by the regression.

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 ordinary least squares between the observed and the fitted compositional data is adopted as the objective function. This involves numerical optimisation since the relationship is non linear. 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, js.compreg, kl.compreg, comp.reg, comp.reg, alfa.reg

Examples

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