MCRLLM 0.0.21

MCRLLM: Multivariate Curve Resolution by Log-Likelihood Maximization

MCRLLM: Multivariate Curve Resolution by Log-Likelihood Maximization.

#Method first presented in:
Lavoie F.B., Braidy N. and Gosselin R. (2016) Including Noise Characteristics in MCR to improve Mapping and Component Extraction from Spectral Images, Chemometrics and Intelligent Laboratory Systems, 153, 40-50.

#Input variable
X(nxk): 2D spectral matrix : n spectra acquired over k energy levels
Note: 3D spectral image can be unfold to 2D matrix prior to analysis.

#Input and output arguments
MCRLLM requires 3 inputs : X dat, number of components to compute (nb) and use of phi exponent.
Refer to paper above for use of phi. To use it: 'phi', if not: 'standard'
decomposition = mcr.mcrllm(X,nb,'phi')
decomposition.iterate(20)
Get Results
allS = decomposition.allS
S_final = decomposition.S
allC = decomposition.allC
C_final = decomposition.C
Sini = decomposition.Sini
allphi = decomposition.allphi