computeLogLikelihood

Vector of residuals after the spectral signature has been subtracted.

obsi

smoothing parameter of the penalised B-spline.

lambda

hyperparameter of the additive noise

prErrNu

prErrSS

Matrix of B-spline basis functions

basisMx

eigenvalues of the Demmler-Reinsch factorisation

eigVal

precMx

orthoganal matrix A from the Demmler-Reinsch factorisation

product of Ru from the Demmler-Reinsch factorisation

ruMx

This is an internal function that is only exposed on the public API for unit testing purposes.
It computes the log-likelihood of the spline and the noise, once the spectral signature has
been subtracted from the observed data. Thus, it can be used with either Lorentzian, Gaussian,
or pseudo-Voigt broadening functions.

Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive
mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS),
using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate
observations of SERRS are highly collinear and lend themselves to a reduced-rank
representation. The GAMM separates the SERRS signal into three components: a
sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline;
and additive white noise. The parameters of each component of the model are estimated
iteratively using SMC. The posterior distributions of the parameters given the observed
spectra are represented as a population of weighted particles.

computeLogLikelihood: Compute the log-likelihood.

Description

Usage

Arguments

Value