mdatools (version 0.5.3)

ldecomp: Linear decomposition of data

Description

Creates an object of ldecomp class.

Usage

ldecomp(scores = NULL, loadings = NULL, residuals = NULL, totvar,
  tnorm = NULL, ncomp.selected = NULL, T2 = NULL, Q2 = NULL,
  cal = TRUE)

Arguments

scores

matrix with score values (nobj x ncomp).

loadings

matrix with loading values (nvar x ncomp).

residuals

matrix with data residuals

totvar

full variance of original data, preprocessed and centered

tnorm

singular values for score normalization

ncomp.selected

number of selected components

T2

matrix with calculated T2 values (e.g. for CV)

Q2

matrix with calculated Q2 values (e.g. for CV)

cal

logical, true if data is for calibration of a LDECOMP based model

Value

• Returns an object (list) of ldecomp class with following fields:
• scores
  matrix with score values (nobj x ncomp).
• residuals
  matrix with data residuals (nobj x nvar).
• T2
  matrix with T2 distances (nobj x ncomp).
• Q2
  matrix with Q2 distances (nobj x ncomp).
• tnorm
  vector with singular values used for scores normalization.
• ncomp.selected
  selected number of components.
• expvar
  explained variance for each component.
• cumexpvar
  cumulative explained variance.
• modpower
  modelling power of variables.

Details

ldecomp is a general class for decomposition 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 and plotting residuals, variances, and so on.

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.