onekpca_loading

This function computes one-sided kernel principal component (one-sided KPC) loading from the result of the "one_kpca" function.

 multivariate

Computing statistical hypothesis testing for loading in principal component analysis (PCA) (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51>), orthogonal smoothed PCA (OS-PCA) (Yamamoto, H. et al. (2021) <doi:10.3390/metabo11030149>), one-sided kernel PCA (Yamamoto, H. (2023) <doi:10.51094/jxiv.262>), partial least squares (PLS) and PLS discriminant analysis (PLS-DA) (Yamamoto, H. et al. (2009) <doi:10.1016/j.chemolab.2009.05.006>), PLS with rank order of groups (PLS-ROG) (Yamamoto, H. (2017) <doi:10.1002/cem.2883>), regularized canonical correlation analysis discriminant analysis (RCCA-DA) (Yamamoto, H. et al. (2008) <doi:10.1016/j.bej.2007.12.009>), multiset PLS and PLS-ROG (Yamamoto, H. (2022) <doi:10.1101/2022.08.30.505949>).

Hiroyuki Yamamoto

loadings

Loadings for Principal Component Analysis and Partial Least
Squares

onekpca_loading function

<dl> <dt>onekpca</dt>
<dd></dd>The following variables (P,T and U) are included in the onekpca object.P : A matrix with one-sided KPCA loading in each column
T : A matrix with one-sided KPCA score for linear side in each column
U : A matrix with one-sied KPCA score for nonlinear side in each column
</dl>

Arguments

Author

One-sided kernel principal component loading — onekpca_loading

<dl>

 <dt>onekpca</dt>
<dd>

The following variables (P,T and U) are included in the onekpca object.

P : A matrix with one-sided KPCA loading in each column
T : A matrix with one-sided KPCA score for linear side in each column
U : A matrix with one-sied KPCA score for nonlinear side in each column
</dd>

</dl>

onekpca_loading: One-sided kernel principal component loading

Description

Usage

Value

Arguments

Author

Details

References

Examples