simuldata: Gaussian Data Generation

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/hdc.R

Description

This function generates two datasets according to the model [AkBkQkDk] of the HDDA gaussian mixture model paramatrisation (see ref.).

Usage

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simuldata(nlearn, ntest, p, K = 3, prop = NULL, d = NULL, a = NULL, b = NULL)

Arguments

nlearn

The size of the learning dataset to be generated.

ntest

The size of the testing dataset to be generated.

p

The number of variables.

K

The number of classes.

prop

The proportion of each class.

d

The dimension of the intrinsic subspace of each class.

a

The value of the main parameter of each class.

b

The noise of each class.

Value

X

The learning dataset.

clx

The class vector of the learning dataset.

Y

The test dataset.

cly

The class vector of the test dataset.

prms

The principal parameters used to generate the datasets.

Author(s)

Laurent Berge, Charles Bouveyron and Stephane Girard

References

Bouveyron, C. Girard, S. and Schmid, C. (2007) “High Dimensional Discriminant Analysis”, Communications in Statistics : Theory and Methods, vol. 36(14), pp. 2607–2623

See Also

hddc, hdda.

Examples

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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)

HDclassif documentation built on Nov. 20, 2019, 1:07 a.m.