Tuning of the divergence based regression for compositional data with compositional data in the covariates side using the alpha-transformation {Compositional} | R Documentation |

Tuning of the divergence based regression for compositional data with compositional data in the covariates side using the *α*-transformation.

klalfapcr.tune(y, x, covar = NULL, nfolds = 10, maxk = 50, a = seq(-1, 1, by = 0.1), folds = NULL, graph = FALSE, tol = 1e-07, maxiters = 50, seed = FALSE)

`y` |
A numerical matrix with compositional data with or without zeros. |

`x` |
A matrix with the predictor variables, the compositional data. Zero values are allowed. |

`covar` |
If you have other continuous covariates put themn here. |

`nfolds` |
The number of folds for the K-fold cross validation, set to 10 by default. |

`maxk` |
The maximum number of principal components to check. |

`a` |
The value 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 |

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

`graph` |
If graph is TRUE (default value) a filled contour plot will appear. |

`tol` |
The tolerance value to terminate the Newton-Raphson procedure. |

`maxiters` |
The maximum number of Newton-Raphson iterations. |

`seed` |
If seed is TRUE the results will always be the same. |

The M-fold cross validation is performed in order to select the optimal values for *α* and k, the number of principal components.
The *α*-transformation is applied to the compositional data first, the first k principal component scores are calcualted and used as predictor variables for the Kullback-Leibler divergence based regression model. This procedure is performed M times during the M-fold cross validation.

A list including:

`mspe` |
A list with the KL divergence for each value of |

`performance` |
A matrix with the KL divergence for each value of |

`best.perf` |
The minimum KL divergence. |

`params` |
The values of |

Initial code by Abdulaziz Alenazi. Modifications by Michail Tsagris.

R implementation and documentation: Abdulaziz Alenazi a.alenazi@nbu.edu.sa and Michail Tsagris mtsagris@uoc.gr.

Alenazi A. (2019). Regression for compositional data with compositioanl data as predictor variables with or without zero values. Journal of Data Science, 17(1): 219-238. http://www.jds-online.com/file_download/688/01+No.10+315+REGRESSION+FOR+COMPOSITIONAL+DATA+WITH+COMPOSITIONAL+DATA+AS+PREDICTOR+VARIABLES+WITH+OR+WITHOUT+ZERO+VALUES.pdf

Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. http://arxiv.org/pdf/1508.01913v1.pdf

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. http://arxiv.org/pdf/1106.1451.pdf

```
kl.alfapcr, cv.tflr, pcr, glm.pcr, alfapcr.tune
```

library(MASS) y <- rdiri( 214, runif(4, 1, 3) ) x <- as.matrix( fgl[, 2:9] ) x <- x / rowSums(x) mod <- klalfapcr.tune(y = y, x = x, a = c(0.7, 0.8) ) mod

[Package *Compositional* version 5.2 Index]