LASSO with compositional predictors using the alpha-transformation {Compositional}

LASSO with compositional predictors using the alpha-transformation

Description

LASSO with compositional predictors using the alpha-transformation.

Usage

alfa.lasso(y, x, a = seq(-1, 1, by = 0.1), model = "gaussian", lambda = NULL,
xnew = NULL)

Arguments

y

A numerical vector or a matrix for multinomial logistic regression.

x

A numerical matrix containing the predictor variables, compositional data, where zero values are allowed..

a

A vector with a grid of values of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If \alpha=0 the isometric log-ratio transformation is applied.

model

The type of the regression model, "gaussian", "binomial", "poisson", "multinomial", or "mgaussian".

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.

xnew

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

Details

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

Value

A list including sublists for each value of \alpha:

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.

Author(s)

Michail Tsagris.

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

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

alfalasso.tune, cv.lasso.klcompreg, lasso.compreg, alfa.knn.reg

Examples

y <- as.matrix(iris[, 1])
x <- rdiri(150, runif(20, 2, 5) )
mod <- alfa.lasso(y, x, a = c(0, 0.5, 1))