Fit the model with Voigt peaks using iterated batch importance sampling (IBIS).
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
)Vector of nwl wavenumbers at which the spetra are observed.
n_y * nwl Matrix of observed Raman spectra.
index of the new observation
List of results from the previous call to ``fitVoigtPeaksSMC`` or ``fitVoigtIBIS``
Vector of n_y nanomolar (nM) dye concentrations for each observation.
identifies to which batch each observation belongs
number of SMC particles to use for the importance sampling distribution.
the target rate of reduction in the effective sample size (ESS).
target acceptance rate for the MCMC kernel
number of iterations of the MCMC kernel
minimum effective sample size, below which the particles are resampled.
target number of unique particles for the MCMC iterations
destination directory to save intermediate results (for long-running computations)
Chopin (2002) "A Sequential Particle Filter Method for Static Models," Biometrika 89(3): 539--551, 10.1093/biomet/89.3.539