fitSpectraMCMC: Fit the model using Markov chain Monte Carlo.
In mooresm/serrsBayes: Bayesian Modelling of Raman Spectroscopy

Description Usage Arguments Value See Also Examples

Fit the model using Markov chain Monte Carlo.

1	fitSpectraMCMC(wl, spc, peakWL, lPriors, sd_mh, niter = 10000, nchains = 4)

`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.
`sd_mh`	Vector of `2 * npeaks` bandwidths for the random walk proposals.
`niter`	number of MCMC iterations per chain.
`nchains`	number of concurrent MCMC chains.

a List containing MCMC samples for the model parameters:

amplitude: niter * nchains * npeaks Array of amplitudes.
scale: niter * nchains * npeaks Array of scale parameters.
sigma: niter * nchains Matrix of standard deviations.
n_acc: The number of RWMH proposals that were accepted.

marginalMetropolisUpdate

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,
                amp.mu=5000, amp.sd=5000, noise.sd=200, noise.nu=4)
rw_bw <- c(100, 100, 2, 2)
result <- fitSpectraMCMC(wavenumbers, spectra, peakLocations, lPriors, rw_bw, 500)
result$n_acc