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This function generates two datasets according to the model [AkBkQkDk] of the HDDA gaussian mixture model paramatrisation (see ref.).
simuldata(nlearn, ntest, p, K = 3, prop = NULL, d = NULL, a = NULL, b = NULL)The learning dataset.
The class vector of the learning dataset.
The test dataset.
The class vector of the test dataset.
The principal parameters used to generate the datasets.
The size of the learning dataset to be generated.
The size of the testing dataset to be generated.
The number of variables.
The number of classes.
The proportion of each class.
The dimension of the intrinsic subspace of each class.
The value of the main parameter of each class.
The noise of each class.
Laurent Berge, Charles Bouveyron and Stephane Girard
Bouveyron, C. Girard, S. and Schmid, C. (2007) “High Dimensional Discriminant Analysis”, Communications in Statistics : Theory and Methods, vol. 36(14), pp. 2607--2623
hddc, hdda.
data <- simuldata(500, 1000, 50, K=5, prop=c(0.2,0.25,0.25,0.15,0.15))
X <- data$X
clx <- data$clx
f <- hdda(X, clx)
Y <- data$Y
cly <- data$cly
e <- predict(f, Y, cly)
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