alpha-RF {CompositionalML}

\alpha-RF

Description

\alpha-RF.

Usage

alfa.rf(xnew, y, x, a = seq(-1, 1, by = 0.1), size = c(1, 2, 3),
depth = c(0, 1), splits = 2:5, R = 500)

Arguments

xnew

A matrix with the new compositional data whose group is to be predicted. Zeros are allowed, but you must be careful to choose strictly positive vcalues of \alpha.

y

The response variable, it can either be a factor (for classification) or a numeric vector (for regression). Depending on the nature of the response variable, the function will proceed with the necessary task.

x

A matrix with the compositional data.

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 a=0, the isometric log-ratio transformation is applied.

size

The minimal node size to split at.

depth

The maximal tree depth. A value of NULL or 0 corresponds to unlimited depth, 1 to tree stumps (1 split per tree).

splits

The number of random splits to consider for each candidate splitting variable.

R

The number of trees.

Details

For each value of \alpha, the compositional data are transformed and then the random forest (RF) is applied for one or more combinations of the hyper-parameters.

Value

A list including:

mod

A list with the results of the RF model for each value of \alpha that includes the RF output (a ranger class object) as provided by the function "ranger" of the package ranger, the configurations used and the predicted values of "xnew".

Author(s)

Michail Tsagris.

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

References

Wright M. N. and Ziegler A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statisrical Software 77:1-17. doi:10.18637/jss.v077.i01.

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf

See Also

alfarf.tune

Examples

x <- as.matrix(iris[, 1:4])
x <- x/ rowSums(x)
y <- iris[, 5]
mod <- alfa.rf(x, y, x, a = c(0, 0.5, 1), size = 3, depth = 1, splits = 2:3, R = 500)
mod