PLS_Toolbox Documentation: mcr | < lwrxy | mdcheck > |
mcr
Purpose
Multivariate curve resolution with constraints.
Synopsis
model = mcr(x,ncomp,options) %calibrate
model = mcr(x,c0,options) %calibrate with explict initial guess
pred = mcr(x,model,options) %predict
options = mcr('options')
Description
MCR decomposes a matrix X as CS such that X = CS + E where E is minimized in a least squares sense. Inputs are the matrix to be decomposed x (size m by n), and either the number of components to extract, ncomp, or the explict initial guess, c0. If c0 is size m by k, where k is the number of factors, then it is assumed to be the initial guess for C. If c0 is size k by n then it is assumed to be the initial guess for S. If m=n then, c0 is assumed to be the initial guess for C. Optional input options is described below.
The output, model, is a standard model structure. The estimated concentrations C are stored in model.loads{2} and the estimated spectra S in model.loads{1}. Sum-squared residuals for samples and variables can be found in model.ssqresiduals{1} and model.ssqresiduals{2}, respectively. See the PLS_Toolbox manual for more information on the MCR method and models.
MCR, by default, uses the alternating least squares (ALS) algorithm. For details on the ALS algorithm and constraints available in MCR, see the ALS reference page.
When called with new data and a model structure, MCR performs a prediction (applies the model to the new data) returning the projection of the new data onto the previously recovered loadings (i.e. estimated spectra).
Options
The default options can be retreived using: options = mcr('options');.
See Also
als, analysis, evolvfa, ewfa, fastnnls, mlpca, parafac, plotloads, plotscores, preprocess
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