sampleMultNormMixture {telescope} | R Documentation |
Telescoping sampling of a Bayesian finite multivariate Gaussian mixture where a prior on the number of components 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.
The parameterizations of the Wishart and inverse Wishart distribution are used as in Frühwirth-Schnatter et al (2021), see also the vignette.
Usage
sampleMultNormMixture(
y,
S,
mu,
Sigma,
eta,
c0,
g0,
G0,
C0,
b0,
B0,
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. |
mu |
A numeric matrix; containing the initial cluster-specific mean values. |
Sigma |
A numeric matrix; containing the initial cluster-specific variance covariance values. |
eta |
A numeric vector; containing the initial cluster sizes. |
c0 |
A numeric vector; hyperparameter of the prior on |
g0 |
A numeric vector; hyperparameter of the prior on |
G0 |
A numeric vector; hyperparameter of the prior on |
C0 |
A numeric vector; initial value of the hyperparameter |
b0 |
A numeric vector; hyperparameter of the prior on |
B0 |
A numeric vector; hyperparameter of the prior on |
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 |
priorOnK |
A named list; providing the prior on the number of components K, see |
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:
-
"Mu"
: sampled component means. -
"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 (ife_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 eithere_0
or\alpha
.
Examples
y <- iris[, 1:4]
z <- iris$Species
r <- ncol(y)
Mmax <- 50
thin <- 1
burnin <- 0
M <- Mmax/thin
Kmax <- 40
Kinit <- 10
G <- "MixStatic"
priorOnE0 <- priorOnE0_spec("G_1_20", 1)
priorOnK <- priorOnK_spec("BNB_143")
R <- apply(y, 2, function(x) diff(range(x)))
b0 <- apply(y, 2, median)
B_0 <- rep(1, r)
B0 <- diag((R^2) * B_0)
c0 <- 2.5 + (r-1)/2
g0 <- 0.5 + (r-1)/2
G0 <- 100 * g0/c0 * diag((1/R^2), nrow = r)
C0 <- g0 * chol2inv(chol(G0))
cl_y <- kmeans(y, centers = Kinit, nstart = 100)
S_0 <- cl_y$cluster
mu_0 <- t(cl_y$centers)
eta_0 <- rep(1/Kinit, Kinit)
Sigma_0 <- array(0, dim = c(r, r, Kinit))
Sigma_0[, , 1:Kinit] <- 0.5 * C0
result <- sampleMultNormMixture(
y, S_0, mu_0, Sigma_0, eta_0,
c0, g0, G0, C0, b0, B0,
M, burnin, thin, Kmax, G, priorOnK, priorOnE0)
K <- result$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)