O2PLSvip: Evaluate the importance of variables in O2PLS models

In gongyh/RamanD2O: RamanD2O: an R/Shiny app designed for the analysis of cellular Ramanome data

Evaluate the importance of variables in O2PLS models

Description

O2PLS-VIP, an approach for variable influence on projection (VIP) in O2PLS models, is a model-based method for judging the importance of variables. For both X and Y data blocks, it generates VIP profiles for (i) the predictive part of the model, (ii) the orthogonal part, and (iii) the total model.

Usage

O2PLSvip(x, y, model)

Arguments

x	Training data of sequence features' relative abundances. Must have the exact same rows (subjects/samples) as y.
y	Training data of metabolite relative abundances. Must have the exact same rows (subjects/samples) as x.
model	List of class "o2m". x and y must be the corresponding training data.

Details

It generates 6 VIPO2PLS profiles in total: 1) Two VIP profiles for the predictive components, which uncover the X- and Y-variables that are more important for the model interpretation in relation to the variation correlated to the Y- and X- data matrices respectively; 2) Two VIP profiles for the orthogonal components for both the X-block and the Y-block severally, profiles that uncover the X- and Y- variables that are more relevant in relation to the variation uncorrelated to the Y- and X- data matrices respectively; 3) Two VIP profiles for the total model (i.e. including the contributions of both predictive and orthogonal components) for both the X- and the Y- blocks severally, these VIP profiles point at the X- and Y- variables that are more significant for the whole model.

Value

A list containing

xvip	For the X-block, the VIP profiles for the predictive part of the model, the orthogonal part, the total model.
yvip	For the Y-block, the VIP profiles for the predictive part of the model, the orthogonal part, the total model.

References

Galindo-Prieto B, Trygg J, Geladi P. A new approach for variable influence on projection (VIP) in O2PLS models. Chemometrics and Intelligent Laboratory Systems 2017; 160: 110-124.

gongyh/RamanD2O documentation built on Dec. 13, 2024, 8:39 a.m.