ldecomp function

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

Creates an object of ldecomp class.

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

Returns

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.

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.