| Naive Bayes classifiers for compositional data using the alpha-transformation {Compositional} | R Documentation |
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 mtsagris@uoc.gr.
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 )