alfa.nb: Naive Bayes classifiers for compositional data using the...

In Compositional: Compositional Data Analysis

Naive Bayes classifiers for compositional data using the \alpha-transformation

Description

Naive Bayes classifiers for compositional data using the \alpha-transformation.

Usage

alfa.nb(xnew, x, ina, a, type = "gaussian")

Arguments

xnew	A matrix with the new compositional predictor data whose class you want to predict. Zeros are allowed.
x	A matrix with the available compositional predictor data. Zeros are allowed.
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".

Details

The \alpha-transformation is applied to the compositional and a naive Bayes classifier is employed.

Value

A matrix with the estimated groups. One column for each value of \alpha.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

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

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

See Also

comp.nb, alfa.rda, alfa.knn, comp.knn, mix.compnorm

Examples

x <- Compositional::rdiri(100, runif(5) )
ina <- rbinom(100, 1, 0.5) + 1
mod <- alfa.nb(x, x, a = c(0, 0.1, 0.2), ina )

Compositional documentation built on Oct. 9, 2024, 5:10 p.m.