serrsBayes (version 0.4-0)

fitSpectraSMC: Fit the model using Sequential Monte Carlo (SMC).

Description

Fit the model using Sequential Monte Carlo (SMC).

Usage

fitSpectraSMC(wl, spc, peakWL, lPriors, conc = rep(1, nrow(spc)),
  npart = 10000, rate = 0.9, minESS = npart/2, destDir = NA)

Arguments

wl

Vector of nwl wavenumbers at which the spetra are observed.

spc

n_y * nwl Matrix of observed Raman spectra.

peakWL

Vector of locations for each peak (cm^-1)

lPriors

List of hyperparameters for the prior distributions.

conc

Vector of n_y nanomolar (nM) dye concentrations for each observation.

npart

number of SMC particles to use for the importance sampling distribution.

rate

the target rate of reduction in the effective sample size (ESS).

minESS

minimum effective sample size, below which the particles are resampled.

destDir

destination directory to save intermediate results (for long-running computations)

Value

a List containing weighted parameter values, known as particles:

weights

Vector of importance weights for each particle.

beta

npart * npeaks Matrix of regression coefficients for the amplitudes.

scale

npart * npeaks Matrix of scale parameters.

sigma

Vector of npart standard deviations.

alpha

bl.knots * n_y * npart Array of spline coefficients for the baseline.

basis

A dense nwl * bl.knots Matrix containing the values of the basis functions.

expFn

npart * nwl Matrix containing the spectral signature.

ess

Vector containing the effective sample size (ESS) at each SMC iteration.

logEvidence

Vector containing the logarithm of the model evidence (marginal likelihood).

accept

Vector containing the Metropolis-Hastings acceptance rate at each SMC iteration.

sd.mh

niter * 2npeaks Matrix of random walk MH bandwidths at each SMC iteration..

References

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

Examples

# NOT RUN {
wavenumbers <- seq(200,600,by=10)
spectra <- matrix(nrow=1, ncol=length(wavenumbers))
peakLocations <- c(300,500)
peakAmplitude <- c(10000,4000)
peakScale <- c(10, 15)
signature <- weightedLorentzian(peakLocations, peakScale, peakAmplitude, wavenumbers)
baseline <- 1000*cos(wavenumbers/200) + 2*wavenumbers
spectra[1,] <- signature + baseline + rnorm(length(wavenumbers),0,200)
lPriors <- list(scale.mu=log(11.6) - (0.4^2)/2, scale.sd=0.4, bl.smooth=10^11, bl.knots=20,
                beta.mu=5000, beta.sd=5000, noise.sd=200, noise.nu=4)
result <- fitSpectraSMC(wavenumbers, spectra, peakLocations, lPriors, npart=500)
# }