ldecomp: Class for storing and visualising linear decomposition of...

In svkucheryavski/mdatools: Multivariate Data Analysis for Chemometrics

Class for storing and visualising linear decomposition of dataset (X = TP' + E)

Description

Creates an object of ldecomp class.

Usage

ldecomp(scores, loadings, residuals, eigenvals, ncomp.selected = ncol(scores))

Arguments

scores	matrix with score values (I x A).
loadings	matrix with loading values (J x A).
residuals	matrix with data residuals (I x J)
eigenvals	vector with eigenvalues for the loadings
ncomp.selected	number of selected components

Details

ldecomp is a general class for storing results of decomposition of dataset in form X = TP' + E. Here, X is a data matrix, T - matrix with scores, P - matrix with loadings and E - matrix with residuals. It is used, for example, for PCA results (pcares), in PLS and other methods. The class also includes methods for calculation of residual distances and explained variance.

There is no need to use the ldecomp manually. For example, when build PCA model with pca or apply it to a new data, the results will automatically inherit all methods of ldecomp.

Value

Returns an object (list) of ldecomp class with following fields:

scores	matrix with score values (I x A).
residuals	matrix with data residuals (I x J).
T2	matrix with score distances (I x A).
Q	matrix with orthogonal distances (I x A).
ncomp.selected	selected number of components.
expvar	explained variance for each component.
cumexpvar	cumulative explained variance.

svkucheryavski/mdatools documentation built on Aug. 25, 2023, 12:27 p.m.