Prediction of a new observation using discriminant analysis based on ESAGdistribution
Prediction of a new observation using discriminant analysis based on ESAG distribution.
esagda.pred(ynew, y, ina)
ynew |
The new observation(s) (unit vector(s)) whose group is to be predicted. |
y |
A data matrix with unit vectors, i.e. spherical directional data. |
ina |
A vector indicating the groups of the data y. |
Prediction of the class of a new spherical vector assuming ESAG distribution.
A vector with the predicted group of each new observation.
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
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.
Paine P.J., Preston S.P., Tsagris M. and Wood A.T.A. (2017). An Elliptically Symmetric Angular Gaussian Distribution. Statistics and Computing, 28(3):689–697.
Mardia, K. V. and Jupp, P. E. (2000). Directional statistics. Chicester: John Wiley & Sons.
m1 <- rnorm(3) m2 <- rnorm(3) + 0.5 y <- rbind( rvmf(100, m1, 3), rvmf(80, m2, 5) ) ina <- c( rep(1,100), rep(2, 80) ) ynew <- rbind(rvmf(10, m1, 10), rvmf(10, m2, 5)) id <- rep(1:2, each = 10) g <- esagda.pred(ynew, y, ina) table(id, g)
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