| Cross validation in von Mises-Fisher discrminant analysis {Directional} | R Documentation |
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 mtsagris@uoc.gr.
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)