fitVoigtPeaksSMC {serrsBayes}

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

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)