mhUpdateVoigt

<code>n_y * nwl</code> Matrix of observed Raman spectra.

spectra

number of observations to use in calculating the likelihood.

kappa

Vector of <code>n_y</code> nanomolar (nM) dye concentrations

conc

Vector of <code>nwl</code> wavenumbers at which the spetra are observed.

wavenum

<code>(4+npeaks*4) x npart</code> Matrix of parameter values for each peak.

thetaMx

<code>(4+npeaks*4) x npart</code> Matrix of logarithms of the parameters.

logThetaMx

lower-triangular Cholesky factorisation of the covariance matrix for the random walk proposals.

mhChol

List of hyperparameters for the prior distributions.

priors

Updates all of the parameters (location, amplitude, std. dev., and scale) using a single Metropolis-
Hastings step, such that the baseline cancels out in the MH ratio, using the marginalisation identity
of Chib (1995).
Note: if <code>npart &gt; 1</code>, then multiple MCMC chains will be executed independently in parallel using
OpenMP. This means that all functions used for the proposal distributions and to evaluate the MH ratio
need to be thread-safe. Specifically, no calls to <code>R::rnorm</code>, <code>R::dnorm</code>, nor their
Rcpp equivalents, can be made from within the parallel portion of the code.

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.

mhUpdateVoigt: Update the parameters of the Voigt peaks using marginal Metropolis-Hastings.

Description

Usage

Arguments

Value

References