unsvmultipls_loading: Unsupervised multiset partial least squares loading

In loadings: Loadings for Principal Component Analysis and Partial Least Squares

Unsupervised multiset partial least squares loading

Description

This function computes unsupervised multiset partial least squares loading from the result of the "unsv_multipls" function.

Usage

unsvmultipls_loading(unsvmpls)

Arguments

unsvmpls

The following variables (P,T) are included in the unsvmpls object. P : A list of matrix with Multiset PLS coefficients in each column for each dataset T : A list of matrix with Multiset PLS scores in each column for each dataset

Details

The unsupervised multiset PLS loading and the p-value by statistical hypothesis testing is added to the unsvmpls object returned by the "unsv_multipls" function in this package.

Value

The return value is a list object that contains the following elements:

Score : A list of matrix with Multiset PLS score for auxiliary variable in each column for each dataset

R : A list of matrix with Unsupervised multiset PLS loadings in each column for each dataset

p.value : A list of matrix with p-value of Unsupervised multiset PLS loading in each column for each dataset

Author(s)

Hiroyuki Yamamoto

References

Yamamoto H. (2022) Multiset partial least squares with rank order of groups for integrating multi-omics data, bioRxiv.

Examples

data(whhl)
X <- whhl$X
tau <- rbind(c(0,1/4,1/4,1/4),c(1/4,0,1/4,1/4),c(1/4,1/4,0,1/4),c(1/4,1/4,1/4,0))

unsvmpls <- unsv_multipls(X,tau)

unsvmpls <- unsvmultipls_loading(unsvmpls)
unsvmpls$loading$R
unsvmpls$loading$p.value

loadings documentation built on May 29, 2024, 8:01 a.m.