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

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Description

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

Usage

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vmf.da(x, ina, fraction = 0.2, R = 1000, seed = FALSE)

Arguments

x

A matrix with the dataset 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 <mtsagris@yahoo.gr> and Giorgos Athineou <athineou@csd.uoc.gr>

References

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

See Also

vmfda.pred, mix.vmf, vmf, dirknn

Examples

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

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