Compositional (version 5.5)

LASSO log-ratio regression with compositional response: LASSO log-ratio regression with compositional response

Description

LASSO log-ratio regression with compositional response.

Usage

lasso.compreg(y, x, alpha = 1, lambda = NULL,
nlambda = 100, xnew = NULL)

Arguments

y

A numerical matrix with compositional data. Zero values are not allowed as the additive log-ratio transformation (alr) is applied to the compositional response prior to implementing the LASSO algortihm.

x

A numerical matrix containing the predictor variables.

alpha

The elastic net mixing parameter, with \(0 \leq \alpha \leq 1\). The penalty is defined as a weighted combination of the ridge and of the Lasso regression. When \(\alpha=1\) LASSO is applied, while \(\alpha=0\) yields the ridge regression.

lambda

This information is copied from the package glmnet. A user supplied lambda sequence. Typical usage is to have the program compute its own lambda sequence based on nlambda and lambda.min.ratio. Supplying a value of lambda overrides this. WARNING: use with care. Avoid supplying a single value for lambda (for predictions after CV use predict() instead). Supply instead a decreasing sequence of lambda values. glmnet relies on its warms starts for speed, and its often faster to fit a whole path than compute a single fit.

nlambda

This information is copied from the package glmnet. The number of \(lambda\) values, default is 100.

xnew

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

Value

A list including:

mod

We decided to keep the same list that is returned by glmnet. So, see the function in that package for more information.

est

If you supply a matrix in the "xnew" argument this will return an array of many matrices with the fitted values, where each matrix corresponds to each value of \(\lambda\).

Details

The function uses the glmnet package to perform LASSO penalised regression. For more details see the function in that package.

References

Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.

Friedman, J., Hastie, T. and Tibshirani, R. (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, Vol. 33(1), 1-22.

See Also

cv.lasso.compreg, lassocoef.plot, lasso.klcompreg, cv.lasso.klcompreg, comp.reg

Examples

# NOT RUN {
y <- as.matrix(iris[, 1:4])
y <- y / rowSums(y)
x <- matrix( rnorm(150 * 30), ncol = 30 )
a <- lasso.compreg(y, x)
# }