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.
covbe
The covariance matrix of the beta coefficients, if bootstrap is chosen, i.e. if B > 1.
est
The fitted of xnew 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 optimization since
the relationship is non linear. There is no log-likelihood.
References
Murteira, Jose MR, and Joaquim JS Ramalho 2016. Regression analysis of multivariate fractional data.
Econometric Reviews 35(4): 515-552.
# NOT RUN {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)
# }