sampleLCA {telescope}

Telescoping sampling of the LCA model where a prior on the number of components K is specified.

Description

• The MCMC scheme is implemented as suggested in Frühwirth-Schnatter et al (2021).

• The priors on the model parameters are specified as in Frühwirth-Schnatter et al (2021), see the vignette for details and notation.

Usage

sampleLCA(
  y,
  S,
  pi,
  eta,
  a0,
  M,
  burnin,
  thin,
  Kmax,
  G = c("MixDynamic", "MixStatic"),
  priorOnK,
  priorOnWeights,
  verbose = FALSE
)

Arguments

y

A numeric matrix; containing the data.

S

A numeric matrix; containing the initial cluster assignments.

pi

A numeric vector; containing the initial cluster-specific success probabilities.

eta

A numeric vector; containing the initial cluster sizes.

a0

A numeric vector; containing the parameters of the prior on the cluster-specific success probabilities.

M

A numeric scalar; specifying the number of recorded iterations.

burnin

A numeric scalar; specifying the number of burn-in iterations.

thin

A numeric scalar; specifying the thinning used for the iterations.

Kmax

A numeric scalar; the maximum number of components.

G

A character string; either "MixDynamic" or "MixStatic".

priorOnK

A named list; providing the prior on the number of components K, see priorOnK_spec().

priorOnWeights

A named list; providing the prior on the mixture weights.

verbose

A logical; indicating if some intermediate clustering results should be printed.

Value

A named list containing:

• "Pi": sampled component-specific success probabilities.

• "Eta": sampled weights.

• "S": sampled assignments.

• "Nk": number of observations assigned to the different components, for each iteration.

• "K": sampled number of components.

• "Kplus": number of filled, i.e., non-empty components, for each iteration.

• "e0": sampled Dirichlet parameter of the prior on the weights (if e_0 is random).

• "alpha": sampled Dirichlet parameter of the prior on the weights (if \alpha is random).

• "acc": logical vector indicating acceptance in the Metropolis-Hastings step when sampling either e_0 or \alpha.

Examples

if (requireNamespace("poLCA", quietly = TRUE)) {
    data("carcinoma", package = "poLCA")
    y <- carcinoma
    N <- nrow(y)
    r <- ncol(y)
    
    Mmax <- 200
    thin <- 1
    burnin <- 100
    M <- Mmax/thin
    Kmax <- 50  
    Kinit <- 10
    
    G <- "MixDynamic"
    priorOnAlpha <- priorOnAlpha_spec("gam_1_2")
    priorOnK <- priorOnK_spec("Pois_1")
    
    cat <- apply(y, 2, max)
    a0 <- rep(1, sum(cat))

    cl_y <- kmeans(y, centers = Kinit, iter.max = 20)
    S_0 <- cl_y$cluster
    eta_0 <- cl_y$size/N

    pi_0 <- do.call("cbind", lapply(1:r, function(j) {
        prop.table(table(S_0, y[, j]), 1)
    }))

    result <- sampleLCA(
        y, S_0, pi_0, eta_0, a0, 
        M, burnin, thin, Kmax, 
        G, priorOnK, priorOnAlpha)

    K <- result$K
    Kplus <- result$Kplus   
    
    plot(seq_along(K), K, type = "l", ylim = c(0, max(K)),  
         xlab = "iteration", main = "",
         ylab = expression("K" ~ "/" ~ K["+"]), col = 1)
    lines(seq_along(Kplus), Kplus, col = 2)
    legend("topright", legend = c("K", expression(K["+"])),
           col = 1:2, lty = 1, box.lwd = 0)
}