fitVoigtPeaksSMC: Fit the model with Voigt peaks using Sequential Monte Carlo...

In serrsBayes: Bayesian Modelling of Raman Spectroscopy

Description

Fit the model with Voigt peaks using Sequential Monte Carlo (SMC).

Usage

fitVoigtPeaksSMC(
  wl,
  spc,
  lPriors,
  conc = rep(1, nrow(spc)),
  npart = 10000,
  rate = 0.9,
  mcAR = 0.234,
  mcSteps = 20,
  minESS = npart/2,
  destDir = NA,
  minPart = npart
)

Arguments

wl	Vector of nwl wavenumbers at which the spetra are observed.
spc	n_y * nwl Matrix of observed Raman spectra.
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).
mcAR	target acceptance rate for the MCMC kernel
mcSteps	number of iterations of the MCMC kernel
minESS	minimum effective sample size, below which the particles are resampled.
destDir	destination directory to save intermediate results (for long-running computations)
minPart	target number of unique particles for the MCMC iterations

Examples

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(scaG.mu=log(11.6) - (0.4^2)/2, scaG.sd=0.4, scaL.mu=log(11.6) - (0.4^2)/2,
   scaL.sd=0.4, bl.smooth=5, bl.knots=20, loc.mu=peakLocations, loc.sd=c(5,5),
   beta.mu=c(5000,5000), beta.sd=c(5000,5000), noise.sd=200, noise.nu=4)
## Not run: 
result <- fitVoigtPeaksSMC(wavenumbers, spectra, lPriors, npart=50, mcSteps=1)

## End(Not run)

serrsBayes documentation built on June 28, 2021, 5:14 p.m.