Cross-validation for the naive Bayes classifiers for compositional data using the alpha-transformation | R Documentation |

Cross-validation for the naive Bayes classifiers for compositional data using the *α*-transformation.

alfanb.tune(x, ina, a = seq(-1, 1, by = 0.1), type = "gaussian", folds = NULL, nfolds = 10, stratified = TRUE, seed = NULL)

`x` |
A matrix with the available data, the predictor variables. |

`ina` |
A vector of data. The response variable, which is categorical (factor is acceptable). |

`a` |
This can be a vector of values or a single number. |

`type` |
The type of naive Bayes, "gaussian", "cauchy" or "laplace". |

`folds` |
A list with the indices of the folds. |

`nfolds` |
The number of folds to be used. This is taken into consideration only if "folds" is NULL. |

`stratified` |
Do you want the folds to be selected using stratified random sampling? This preserves the analogy of the samples of each group. Make this TRUE if you wish. |

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

This function estimates the performance of the naive Bayes classifier for each value of *α* of the *α*-transformation.

A list including:

`crit` |
A vector whose length is equal to the number of k and is the accuracy metric for each k. For the classification case it is the percentage of correct classification. |

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

Friedman J., Hastie T. and Tibshirani R. (2017). The elements of statistical learning. New York: Springer.

```
alfa.nb, alfarda.tune, compknn.tune, cv.dda, cv.compnb
```

x <- as.matrix(iris[, 1:4]) x <- x / rowSums(x) mod <- alfanb.tune(x, ina = iris[, 5], a = c(0, 0.1, 0.2) )

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