plsda_loading

This function computes partial least squares discriminant analysis (PLS-DA) loading from the result of the 'pls_da' 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

plsda_loading function

<dl> <dt>plsda</dt>
<dd></dd>The following variables (P,T) are included in the plsda object.
P: A matrix containing the PLS-DA loadings for each explanatory variable in the columns, before transformation.
T : A matrix with PLS-DA score for explanatory variable in each column
</dl>

Arguments

Author

Partial least squares discriminant analysis (PLS-DA) loading — plsda_loading

<dl>

 <dt>plsda</dt>
<dd>

The following variables (P,T) are included in the plsda object.
P: A matrix containing the PLS-DA loadings for each explanatory variable in the columns, before transformation.
T : A matrix with PLS-DA score for explanatory variable in each column
</dd>

</dl>

plsda_loading: Partial least squares discriminant analysis (PLS-DA) loading

Description

Usage

Value

Arguments

Author

Details

References

Examples