Non linear least squares regression for compositional data {Compositional}

Non linear least squares regression for compositional data

Description

Non linear least squares regression for compositional data.

Usage

ols.compreg(y, x, con = TRUE, B = 1, ncores = 1, xnew = NULL)

Arguments

y

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

x

A matrix or a data frame with the predictor variable(s).

con

If this is TRUE (default) then the constant term is estimated, otherwise the model includes no constant term.

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.

Details

The ordinary least squares between the observed and the fitted compositional data is adopted as the objective function. This involves numerical optimization since the relationship is non linear. There is no log-likelihood.

Value

A list including:

runtime

The time required by the regression.

beta

The beta coefficients.

covbe

The covariance matrix of the beta coefficients. If B=1, this is based on the observed information (Hessian matrix), otherwise if B> this is the bootstrap estimate.

est

The fitted of xnew if xnew is not NULL.

Author(s)

Michail Tsagris.

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

References

Murteira, Jose MR, and Joaquim JS Ramalho 2016. Regression analysis of multivariate fractional data. Econometric Reviews 35(4): 515-552.

See Also

diri.reg, js.compreg, kl.compreg, comp.reg, comp.reg, alfa.reg

Examples

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