simulate_classes {plsVarSel} | R Documentation |
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 Sæbø.
References
T. Mehmood, K.H. Liland, L. Snipen, S. Sæbø, 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))