fitVoigtIBIS {serrsBayes}

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, doi: 10.1093/biomet/89.3.539