simulate_classes: Simulate classes

In khliland/plsVarSel: Variable Selection in Partial Least Squares

Simulate classes

Description

Simulate multivariate normal data.

Usage

simulate_classes(p, n1, n2)

simulate_data(dims, n1 = 150, n2 = 50)

Arguments

p	integer number of variables.
n1	integer number of samples in each of two classes in training/calibration data.
n2	integer number of samples in each of two classes in test/validation data.
dims	a 10 element vector of group sizes.

Details

The class simulation is a straigh forward simulation of mulitvariate normal data into two classes for training and test data, respectively. The data simulation uses a strictly structured multivariate normal simulation for with continuous response data.

Value

Returns a list of predictor and response data for training and testing.

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve S?b?.

References

T. Mehmood, K.H. Liland, L. Snipen, S. S?b?, A review of variable selection methods in Partial Least Squares Regression, Chemometrics and Intelligent Laboratory Systems 118 (2012) 62-69. T. Mehmood, S. Sæbø, K.H. Liland, Comparison of variable selection methods in partial least squares regression, Journal of Chemometrics 34 (2020) e3226.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

str(simulate_classes(5,4,4))

khliland/plsVarSel documentation built on April 24, 2024, 11:21 a.m.