data(toydata) # simulated data
data(stanfit) # output of modelFit or main.modelFit
coda.fit <- coda.obj(stanfit)
data.simul <- toydata$Y.simul$Y
N = nrow(data.simul)
D = toydata$wlu$D
P = ncol(data.simul)
## PCA in the histogram basis
obs <- toydata$X
times <- toydata$t
pca.data <- pca.Deville(obs, times, t.range = c(min(times), max(times)), breaks = 15)
## Post-processing landmark information
rotation <- toydata$wlu$Q # rotation matrix
real.W <- toydata$wlu$W # PCA-determined latent factors
real.B <- t(pca.data$Cp[, 1:(toydata$wlu$D)]) # PCA-determined scores
codafit.clean <- clean.mcmc(N, P, D, coda.fit, rotation, real.W, real.B)
head(codafit.clean)Run the code above in your browser using DataLab