Fit the model with Voigt peaks using iterated batch importance sampling (IBIS).

Usage

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

Arguments

wl: Vector of nwl wavenumbers at which the spetra are observed.
spc: n_y * nwl Matrix of observed Raman spectra.
n: index of the new observation
lResult: List of results from the previous call to “fitVoigtPeaksSMC“ or “fitVoigtIBIS“
conc: Vector of n_y nanomolar (nM) dye concentrations for each observation.
batch: identifies to which batch each observation belongs
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.
minPart: target number of unique particles for the MCMC iterations
destDir: destination directory to save intermediate results (for long-running computations)

References

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