vmf.da: Cross validation for estimating the classification rate of a...

In Directional: A Collection of Functions for Directional Data Analysis

Cross validation for estimating the classification rate of a discrminant analysis for directional data assuming a von Mises-Fisher distribution

Description

Cross validation for estimating the classification rate of a discrminant analysis for directional data assuming a von Mises-Fisher distribution.

Usage

vmf.da(x, ina, fraction = 0.2, R = 200, seed = NULL)

Arguments

x	A matrix with the data in Eulcidean coordinates, i.e. unit vectors.
ina	A variable indicating the groupings.
fraction	The fraction of data to be used as test set.
R	The number of repetitions.
seed	If seed is TRUE, the results will always be the same.

Details

A repeated cross validation procedure is performed to estimate the rate of correct classification.

Value

A list including:

percent	The estimated percent of correct classification and two estimated standard deviations. The one is the standard devation of the rates and the other is assuming a binomial distribution.
ci	Three types of confidence intervals, the standard one, another one based on the binomial distribution and the third one is the empirical one, which calcualtes the upper and lower 2.5% of the rates.

Author(s)

Michail Tsagris.

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

References

Tsagris M. and Alenazi A. (2019). Comparison of discriminant analysis methods on the sphere. Communications in Statistics: Case Studies, Data Analysis and Applications, 5(4): 467–491.

Morris J. E. and Laycock P. J. (1974). Discriminant analysis of directional data. Biometrika, 61(2): 335–341.

See Also

vmfda.pred, mixvmf.mle, vmf.mle, dirknn

Examples

x <- rvmf(100, rnorm(4), 15)
ina <- rep(1:2, each = 50)
vmf.da(x, ina, fraction = 0.2, R = 200)

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