alfa.comp.rf {CompositionalRF} | R Documentation |
Compositional Random Forests using the alpha-transformation
Description
Compositional Random Forests using the alpha-transformation.
Usage
alfa.comp.rf(xnew = x, y, x, a = seq(-1, 1, by = 0.1), ntrees,
nfeatures, minleaf, ncores = 1)
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. |
a |
A vector of |
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. |
Details
The compositional data are first using the \alpha
-transformation and then the multivariate random forest algorithm of Rahman, Otridge and Pal (2017) is applied.
Value
A list with the estimated compositional response values, one matrix for each value of \alpha
.
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
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.
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
Examples
y <- as.matrix(iris[, 1:4])
y <- y/ rowSums(y)
x <- matrix( rnorm(150 * 10), ncol = 10 )
mod <- alfa.comp.rf(x[1:10, ], y, x, a = 0.5, ntrees = 2, nfeatures = 5, minleaf = 10)
mod