CompositionalRF (version 1.3)

cv.comprf: Cross-Validation of the Compositional Random Forests

Description

Cross-Validation of the Compositional Random Forests.

Usage

cv.comprf(y, x, ntrees = c(50, 100, 500, 1000), nfeatures, minleaf,
folds = NULL, nfolds = 10, seed = NULL, ncores = 1)

Value

A list including:

kl

A matrix with the configurations of hyper-parameters tested and the estimated Kullback-Leibler divergence, for each configuration.

js

A matrix with the configurations of hyper-parameters tested and the estimated Jensen-Shannon divergence, for each configuration.

Arguments

y

The response compositional data. Zero values are not allowed.

x

A matrix with the predictor variables data.

ntrees

A vector with the possible number of trees to consider each time.

nfeatures

A vector with the number of randomly selected predictor variables considered for a split in each regression tree node.

minleaf

A vector with the minimum number of observations in the leaf node.

folds

If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.

nfolds

The number of folds in the cross validation.

seed

You can specify your own seed number here or leave it NULL.

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.

Author

Michail Tsagris.

R implementation and documentation: Michail Tsagris .

Details

K-fold cross-validation for the multivariate random forest with compositional responses is performed.

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

comp.rf

Examples

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