COMIX (version 1.0.2)

relabelChain: This function relabels the chain to avoid label switching issues.

Description

This function relabels the chain to avoid label switching issues.

Usage

relabelChain(res)

Value

An object of class COMIX where res$chain$t is replaced with the new labels.

Arguments

res

An object of class COMIX.

Examples

library(COMIX)
# Number of observations for each sample (row) and cluster (column):
njk <- 
  matrix(
    c(
      150, 300,
      250, 200
    ),
    nrow = 2,
    byrow = TRUE
  )

# Dimension of data:
p <- 3

# Scale and skew parameters for first cluster:
Sigma1 <- matrix(0.5, nrow = p, ncol = p) + diag(0.5, nrow = p)
alpha1 <- rep(0, p)
alpha1[1] <- -5
# location parameter for first cluster in first sample:
xi11 <- rep(0, p)
# location parameter for first cluster in second sample (aligned with first):
xi21 <- rep(0, p)

# Scale and skew parameters for second cluster:
Sigma2 <- matrix(-1/3, nrow = p, ncol = p) + diag(1 + 1/3, nrow = p)
alpha2 <- rep(0, p)
alpha2[2] <- 5
# location parameter for second cluster in first sample:
xi12 <- rep(3, p)
# location parameter for second cluster in second sample (misaligned with first):
xi22 <- rep(4, p)

# Sample data:
set.seed(1)
Y <- 
  rbind(
    sn::rmsn(njk[1, 1], xi = xi11, Omega = Sigma1, alpha = alpha1),
    sn::rmsn(njk[1, 2], xi = xi12, Omega = Sigma2, alpha = alpha2),
    sn::rmsn(njk[2, 1], xi = xi21, Omega = Sigma1, alpha = alpha1),
    sn::rmsn(njk[2, 2], xi = xi22, Omega = Sigma2, alpha = alpha2)
  )

C <- c(rep(1, rowSums(njk)[1]), rep(2, rowSums(njk)[2]))

prior <- list(zeta = 1, K = 10)
pmc <- list(naprt = 5, nburn = 200, nsave = 200) # Reasonable usage
pmc <- list(naprt = 5, nburn = 2, nsave = 5) # Minimal usage for documentation
# Fit the model:
res <- comix(Y, C, pmc = pmc, prior = prior)

# Relabel to resolve potential label switching issues:
res_relab <- relabelChain(res)

# Generate calibrated data:
cal <- calibrateNoDist(res_relab)

# Compare raw and calibrated data: (see plot in vignette)
# par(mfrow=c(1, 2))
# plot(Y, col = C, xlim = range(Y[,1]), ylim = range(Y[,2]) )

# Get posterior estimates for the model parameters:
res_summary <- summarizeChain(res_relab)
# Check for instance, the cluster assignment labels:
table(res_summary$t)
# Indeed the same as 
colSums(njk)

# Or examine the skewness parameter for the non-trivial clusters:
res_summary$alpha[ , unique(res_summary$t)]
# And compare those to
cbind(alpha1, alpha2)

# (see vignette for a more detailed example)