Posterior distribution for pseudo-Voigt parameters, obtained by running `fitVoigtPeaksSMC` on a spectrum from Gracie et al. (Anal. Chem., 2016). 1000 SMC particles with 32 peaks. For details, see the vignette.

result

Format

A list containing 15 variables:

weights

normalised importance weights for each particle

location

location parameters of 32 peaks

beta

amplitudes of 32 peaks

scale_G

scale of the Gaussian (RBF) broadening

scale_L

scale of the Lorentzian (Cauchy) broadening

sigma

standard deviation of the additive white noise

lambda

smoothing parameter of the cubic B-splines

priors

List of informative priors

ess

history of the effective sample size

kappa

history of the likelihood tempering

accept

history of Metropolis-Hastings acceptance rates

mhSteps

history of Metropolis-Hastings steps

times

history of times for each SMC iteration

time

computation time taken by the SMC algorithm