| Type: | Package |
| Title: | Machine Learning with Compositional Data |
| Version: | 1.0 |
| Date: | 2024-03-13 |
| Author: | Michail Tsagris [aut, cre] |
| Maintainer: | Michail Tsagris <mtsagris@uoc.gr> |
| Depends: | R (≥ 4.0) |
| Imports: | Boruta, Compositional, doParallel, e1071, foreach, graphics, ranger, Rfast, Rfast2, stats |
| Description: | Machine learning algorithms for predictor variables that are compositional data and the response variable is either continuous or categorical. Specifically, the Boruta variable selection algorithm, random forest, support vector machines and projection pursuit regression are included. Relevant papers include: Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451> and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics–Theory and Methods, 52(16): 5535–5567. <doi:10.1080/03610926.2021.2014890>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2024-03-13 12:23:56 UTC; mtsag |
| Repository: | CRAN |
| Date/Publication: | 2024-03-14 12:40:02 UTC |
Machine Learning with Compositional Data
Description
Description: Machine learning algorithms for predictor variables that are compositional data and the response variable is either continuous or categorical. Specifically, the Boruta variable selection algorithm, random forest, support vector machines and projection pursuit regression are included. Relevant papers include: Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics–Theory and Methods, 52(16): 5535–5567.
Details
| Package: | CompositionalML |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2024-03-13 |
| License: | GPL-2 |
Maintainers
Michail Tsagris <mtsagris@uoc.gr>
Author(s)
Michail Tsagris mtsagris@uoc.gr.
References
Alenazi A. (2023). A review of compositional data analysis and recent advances. Communications in Statistics–Theory and Methods, 52(16): 5535–5567.
Friedman J. H. and Stuetzle W. (1981). Projection pursuit regression. Journal of the American Statistical Association, 76, 817-823. doi: 10.2307/2287576.
Friedman Jerome, Trevor Hastie and Robert Tibshirani (2009). The elements of statistical learning, 2nd edition. Springer, Berlin.
Chang Chih-Chung and Lin Chih-Jen: LIBSVM: a library for Support Vector Machines https://www.csie.ntu.edu.tw/~cjlin/libsvm/
Kursa M. B. and Rudnicki W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11).
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. Fourth International International Workshop on Compositional Data Analysis.
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.
Tuning the parameters of the\alpha-PPR
Description
Tuning the parameters of the\alpha-PPR.
Usage
alfappr.tune(y, x, a = seq(-1, 1, by = 0.1), nterms = 1:10, ncores = 1,
folds = NULL, nfolds = 10, seed = NULL, graph = FALSE)
Arguments
y |
The response variable, a numerical vector. |
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. |
nterms |
The number of terms to include in the model. |
ncores |
The number of cores to use. If more 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. |
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. |
graph |
If graph is TRUE (default value) a plot will appear. |
Details
K-fold cross-validation of the \alpha-PPR with compositional predictor
variables is performed to select the optimal value of \alpha and the numer of
terms in the PPR.
Value
If graph is true, a graph with the estimated performance for each value of \alpha.
A list including:
per |
A vector with the estimated performance for each value of |
performance |
A vector with the optimal performance and the optimal number of terms. |
best_a |
The value of |
runtime |
The time required by the cross-validation procedure. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Friedman J. H. and Stuetzle W. (1981). Projection pursuit regression. Journal of the American Statistical Association, 76, 817-823. doi: 10.2307/2287576.
Friedman Jerome, Trevor Hastie and Robert Tibshirani (2009). The elements of statistical learning, 2nd edition. Springer, Berlin.
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
x <- as.matrix(iris[, 1:3])
x <- x/ rowSums(x)
y <- iris[, 4]
mod <- alfappr.tune(y, x, a = c(0, 0.5, 1), nterms = c(2, 3))
mod
Tuning the parameters of the \alpha-RF
Description
Tuning the parameters of the \alpha-RF.
Usage
alfarf.tune(y, x, a = seq(-1, 1, by = 0.1), size = c(1, 2, 3),
depth = c(0, 1), splits = 2:5, R = 500, ncores = 1, folds = NULL,
nfolds = 10, stratified = TRUE, seed = NULL, graph = FALSE)
Arguments
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. |
ncores |
The number of cores to use. If more 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. |
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. |
stratified |
Do you want the folds to be created in a stratified way? TRUE or FALSE. |
seed |
You can specify your own seed number here or leave it NULL. |
graph |
If graph is TRUE (default value) a plot will appear. |
Details
K-fold cross-validation of the \alpha-RF with compositional predictor
variables is performed to select the optimal value of \alpha and the optimal
configutrations of the random forest (RF).
Value
If graph is true, a graph with the estimated performance for each value of \alpha.
A list including:
per |
A vector with the estimated performance for each value of |
performance |
A vector with the optimal performance and the optimal combinations of the hyper-parameters of the RF. |
best_a |
The value of |
runtime |
The time required by the cross-validation procedure. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
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.
Friedman Jerome, Trevor Hastie and Robert Tibshirani (2009). The elements of statistical learning, 2nd edition. Springer, Berlin.
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
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
Tuning the parameters of the \alpha-SVM
Description
Tuning the parameters of the \alpha-SVM.
Usage
alfasvm.tune(y, x, a = seq(-1, 1, by = 0.1), cost = seq(0.2, 2, by = 0.2), gamma = NULL,
ncores = 1, folds = NULL, nfolds = 10, stratified = TRUE, seed = NULL, graph = FALSE)
Arguments
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. |
cost |
A grid of values for the cost of constraints violation. The cost is the "C"-constant of the regularization term in the Lagrange formulation. |
gamma |
A grid of values for the |
ncores |
The number of cores to use. If more 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. |
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. |
stratified |
Do you want the folds to be created in a stratified way? TRUE or FALSE. |
seed |
You can specify your own seed number here or leave it NULL. |
graph |
If graph is TRUE (default value) a plot will appear. |
Details
K-fold cross validation is performed to select the optimal parameters for the SVM and also estimate the rate of accuracy. For continuous responses the estimated performance translates to the MSE, while for categorical responses (factors) this is the accuracy (percentage of crrect classification).
Value
If graph is true, a graph with the estimated performance for each value of \alpha.
A list including:
per |
A vector with the estimated performance for each value of |
performance |
A vector with the optimal performance and the optimal combinations of cost and |
best_a |
The value of |
runtime |
The time required by the cross-validation procedure. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Chang Chih-Chung and Lin Chih-Jen: LIBSVM: a library for Support Vector Machines https://www.csie.ntu.edu.tw/~cjlin/libsvm/
Friedman Jerome, Trevor Hastie and Robert Tibshirani (2009). The elements of statistical learning, 2nd edition. Springer, Berlin.
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
x <- as.matrix(iris[, 1:4])
x <- x/ rowSums(x)
y <- iris[, 5]
mod <- alfasvm.tune(y, x, a = c(0, 0.5, 1), cost = c(0.2, 0.4), gamma = c(0.1, 0.2) )
mod
\alpha-PPR with compositional predictor variables
Description
\alpha-PPR with compositional predictor variables.
Usage
alfa.ppr(xnew, y, x, a = seq(-1, 1, by = 0.1), nterms = 1:10)
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 |
y |
The response variable, a numerical vector. |
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. |
nterms |
The number of terms to include in the model. |
Details
This is the standard projection pursuit regression (PPR) applied to the
\alpha-transformed compositional predictors.
See the built-in function "ppr" for more details.
Value
A list including:
mod |
A list with the results of the PPR model for each value of |
est |
A list with the predicted response values of "xnew" for each value of |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Friedman J. H. and Stuetzle W. (1981). Projection pursuit regression. Journal of the American Statistical Association, 76, 817-823. doi: 10.2307/2287576.
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
x <- as.matrix(iris[, 1:3])
x <- x/ rowSums(x)
y <- iris[, 4]
mod <- alfa.ppr(x, y, x, a = c(0, 0.5, 1), nterms = c(2, 3))
mod
\alpha-Boruta variable selection
Description
\alpha-Boruta variable selection.
Usage
alfa.boruta(y, x, a = seq(-1, 1, by = 0.1), runs = 100 )
Arguments
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. |
runs |
Maximal number of importance source runs. You may increase it to avoid variables being characterised as "Tentative". |
Details
For each value of \alpha, the compositional data are transformed and then
the Boruta variable selection algorithm is applied.
Value
A list with the results of the Boruta variable selection algortihm for each value of \alpha
as returned by the function "Boruta" of the package Boruta. The important elements
are these (all the items returned by the function are of course included):
finalDecision |
A factor of three values: "Confirmed", "Rejected" or "Tentative"", for each variable, containing the final result of the variable selection. |
ImpHistory |
A data frame of importances of variables gathered in each importance source run. Beside the importances, it contains maximal, mean and minimal importance of shadow variables in each run. Rejected attributes get -Inf importance. Set to NULL if holdHistory was given FALSE. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Kursa M. B. and Rudnicki W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11).
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
x <- as.matrix(iris[, 1:4])
x <- x/ rowSums(x)
y <- iris[, 5]
mod <- alfa.boruta(y, x, a = c(0, 0.5))
mod
\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 |
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 |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
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
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
\alpha-SVM
Description
\alpha-SVM.
Usage
alfa.svm(xnew, y, x, a = seq(-1, 1, by = 0.1), cost = seq(0.2, 2, by = 0.2), gamma = NULL)
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 |
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. |
cost |
A grid of values for the cost of constraints violation. The cost is the "C"-constant of the regularization term in the Lagrange formulation. |
gamma |
A grid of values for the |
Details
For each value of \alpha, the compositional data are transformed and then
the SVM is applied for one or more combinations of the cost and \gamma parameters.
Value
A list including:
mod |
A list with the results of the SVM model for each value of |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Chang Chih-Chung and Lin Chih-Jen: LIBSVM: a library for Support Vector Machines https://www.csie.ntu.edu.tw/~cjlin/libsvm/
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
x <- as.matrix(iris[, 1:4])
x <- x/ rowSums(x)
y <- iris[, 5]
mod <- alfa.svm(x, y, x, a = c(0, 0.5, 1), cost = c(0.2, 0.4), gamma = c(0.1, 0.2) )
mod