pls_svd

This function performs partial least squares. In this function, data matrix for explanatory variable is automatically scaled to zero mean and unit variance (i.e. autoscaling) for each variables.

 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

pls_svd function

<dl> <dt>X</dt>
<dd>Data matrix of explanatory variables that include variables in each columns.</dd> <dt>Y</dt>
<dd>Dummy matrix that include group information 0,1 in each columns.</dd></dl>

Arguments

Author

Partial least squares — pls_svd

<dl>

 <dt>X</dt>
<dd>Data matrix of explanatory variables that include variables in each columns.</dd>

 <dt>Y</dt>
<dd>Dummy matrix that include group information 0,1 in each columns.</dd>

</dl>

pls_svd: Partial least squares

Description

Usage

Value

Arguments

Author

Details

References

Examples