fitSpectraSMC: Fit the model using Sequential Monte Carlo (SMC).
In serrsBayes: Bayesian Modelling of Raman Spectroscopy

Description Usage Arguments Value References Examples

Fit the model using Sequential Monte Carlo (SMC).

fitSpectraSMC(
  wl,
  spc,
  peakWL,
  lPriors,
  conc = rep(1, nrow(spc)),
  npart = 10000,
  rate = 0.9,
  minESS = npart/2,
  destDir = NA
)

`wl`	Vector of `nwl` wavenumbers at which the spetra are observed.
`spc`	`n_y * nwl` Matrix of observed Raman spectra.
`peakWL`	Vector of locations for each peak (cm^-1)
`lPriors`	List of hyperparameters for the prior distributions.
`conc`	Vector of `n_y` nanomolar (nM) dye concentrations for each observation.
`npart`	number of SMC particles to use for the importance sampling distribution.
`rate`	the target rate of reduction in the effective sample size (ESS).
`minESS`	minimum effective sample size, below which the particles are resampled.
`destDir`	destination directory to save intermediate results (for long-running computations)

a List containing weighted parameter values, known as particles:

weights: Vector of importance weights for each particle.
beta: npart * npeaks Matrix of regression coefficients for the amplitudes.
scale: npart * npeaks Matrix of scale parameters.
sigma: Vector of npart standard deviations.
alpha: bl.knots * n_y * npart Array of spline coefficients for the baseline.
basis: A dense nwl * bl.knots Matrix containing the values of the basis functions.
expFn: npart * nwl Matrix containing the spectral signature.
ess: Vector containing the effective sample size (ESS) at each SMC iteration.
logEvidence: Vector containing the logarithm of the model evidence (marginal likelihood).
accept: Vector containing the Metropolis-Hastings acceptance rate at each SMC iteration.
sd.mh: niter * 2npeaks Matrix of random walk MH bandwidths at each SMC iteration..

Chopin (2002) "A Sequential Particle Filter Method for Static Models," Biometrika 89(3): 539–551, doi: 10.1093/biomet/89.3.539

wavenumbers <- seq(200,600,by=10)
spectra <- matrix(nrow=1, ncol=length(wavenumbers))
peakLocations <- c(300,500)
peakAmplitude <- c(10000,4000)
peakScale <- c(10, 15)
signature <- weightedLorentzian(peakLocations, peakScale, peakAmplitude, wavenumbers)
baseline <- 1000*cos(wavenumbers/200) + 2*wavenumbers
spectra[1,] <- signature + baseline + rnorm(length(wavenumbers),0,200)
lPriors <- list(scale.mu=log(11.6) - (0.4^2)/2, scale.sd=0.4, bl.smooth=10^11, bl.knots=20,
                beta.mu=5000, beta.sd=5000, noise.sd=200, noise.nu=4)
## Not run: 
result <- fitSpectraSMC(wavenumbers, spectra, peakLocations, lPriors, npart=500)

## End(Not run)