fitVoigtIBIS: Fit the model with Voigt peaks using iterated batch...

In serrsBayes: Bayesian Modelling of Raman Spectroscopy

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

Description

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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/89.3.539")}

serrsBayes documentation built on Oct. 21, 2026, 5:14 p.m.