vmnb.pred: Prediction with some naive Bayes classifiers for circular...

In Directional: A Collection of Functions for Directional Data Analysis

Prediction with some naive Bayes classifiers for circular data

Description

Prediction with some naive Bayes classifiers for circular data.

Usage

vmnb.pred(xnew, mu, kappa, ni)
spmlnb.pred(xnew, mu1, mu2, ni)

Arguments

xnew	A numerical matrix with new predictor variables whose group is to be predicted. Each column refers to an angular variable.
mu	A matrix with the mean vectors expressed in radians.
mu1	A matrix with the first set of mean vectors.
mu2	A matrix with the second set of mean vectors.
kappa	A matrix with the kappa parameters for the vonMises distribution or with the norm of the mean vectors for the circular angular Gaussian distribution.
ni	The sample size of each group in the dataset.

Details

Each column is supposed to contain angular measurements. Thus, for each column a von Mises distribution or an circular angular Gaussian distribution is fitted. The product of the densities is the joint multivariate distribution.

Value

A numerical vector with 1, 2, ... denoting the predicted group.

Author(s)

Michail Tsagris.

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

See Also

vm.nb, weibullnb.pred

Examples

x <- matrix( runif( 100, 0, 1 ), ncol = 2 )
ina <- rbinom(50, 1, 0.5) + 1
a <- vm.nb(x, x, ina)
a2 <- vmnb.pred(x, a$mu, a$kappa, a$ni)

Directional documentation built on Oct. 12, 2023, 1:07 a.m.