CompositionalRF (version 1.3)

comp.rf: Compositional Random Forests

Description

Compositional Random Forests.

Usage

comp.rf(xnew = x, y, x, type = "alr", ntrees, nfeatures, minleaf, ncores = 1)

Value

A matrix with the estimated compositional response values.

Arguments

xnew

A matrix with the new predictor variables whose compositional response values are to be predicted.

y

The response compositional data. Zero values are not allowed.

x

A matrix with the predictor variables data.

type

If the responses are alreay transformed with the additive log-ratio transformation type 0, otherwise, if they are compositional data, leave it equal to "alr", so that the data will be transformed.

ntrees

The number of trees to construct in the random forest.

nfeatures

The number of randomly selected predictor variables considered for a split in each regression tree node, which must be less than the number of input precictors.

minleaf

Minimum number of observations in the leaf node. If a node has less than or equal to minleaf observations, there will be no splitting in that node and this node will be considered as a leaf node. The number evidently must be less than or equal to the sample size.

ncores

The number of cores to use. If greater than 1, parallel computing will take place. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down the process. The default is 1, meaning that code is executed serially.

Author

Michail Tsagris.

R implementation and documentation: Michail Tsagris and Christos Adam .

Details

The compositional are first log-transformed using the additive log-ratio transformation and then the multivariate random forest algorithm of Rahman, Otridge and Pal (2017) is applied.

References

Rahman R., Otridge J. and Pal R. (2017). IntegratedMRF: random forest-based framework for integrating prediction from different data types. Bioinformatics, 33(9): 1407--1410.

Segal M. and Xiao Y. (2011). Multivariate random forests. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1): 80--87.

See Also

cv.comprf

Examples

y <- as.matrix(iris[, 1:4])
y <- y/ rowSums(y)
x <- matrix( rnorm(150 * 10), ncol = 10 )
mod <- comp.rf(x[1:10, ], y, x, ntrees = 2, nfeatures = 5, minleaf = 10)
mod