View source: R/alfaridge.tune.R

Cross validation for the ridge regression with compositional data as predictor using the alpha-transformation | R Documentation |

Cross validation for the ridge regression is performed.
There is an option for the GCV criterion which is automatic. The predictor variables are compositional data and the *α*-transformation is applied first.

alfaridge.tune(y, x, nfolds = 10, a = seq(-1, 1, by = 0.1), lambda = seq(0, 2, by = 0.1), folds = NULL, ncores = 1, graph = TRUE, col.nu = 15, seed = NULL)

`y` |
A numeric vector containing the values of the target variable. If the values are proportions or percentages, i.e. strictly within 0 and 1 they are mapped into R using the logit transformation. |

`x` |
A numeric matrix containing the compositional data, i.e. the predictor variables. Zero values are allowed. |

`nfolds` |
The number of folds in the cross validation. |

`a` |
A vector with the a grid of values of |

`lambda` |
A vector with the a grid of values of |

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

`ncores` |
The number of cores to use. If it is more than 1 parallel computing is performed. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down th process. |

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

`col.nu` |
A number parameter for the filled contour plot, taken into account only if graph is TRUE. |

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

A k-fold cross validation is performed.

If graph is TRUE a fileld contour a filled contour will appear. A list including:

`mspe` |
The MSPE where rows correspond to the |

`best.par` |
The best pair of |

`performance` |
The minimum mean squared error of prediction. |

`runtime` |
The run time of the cross-validation procedure. |

Michail Tsagris.

R implementation and documentation: Giorgos Athineou <gioathineou@gmail.com> and Michail Tsagris mtsagris@uoc.gr.

Hoerl A.E. and R.W. Kennard (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1):55-67.

Brown P. J. (1994). Measurement, Regression and Calibration. Oxford Science Publications.

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

` alfa.ridge, ridge.tune `

library(MASS) y <- as.vector(fgl[, 1]) x <- as.matrix(fgl[, 2:9]) x <- x / rowSums(x) alfaridge.tune( y, x, nfolds = 10, a = seq(0.1, 1, by = 0.1), lambda = seq(0, 1, by = 0.1) )

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