stayMCMCupdate: stayMCMCupdate

In bpgmm: Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models

stayMCMCupdate

Description

stayMCMCupdate

Usage

stayMCMCupdate(X, thetaYList, ZOneDim, hparam, qVec, qnew, dVec, sVec,
  constraint, clusInd)

Arguments

X	X
thetaYList	thetaYList
ZOneDim	ZOneDim
hparam	hparam
qVec	qVec
qnew	qnew
dVec	dVec
sVec	sVec
constraint	constraint
clusInd	clusInd

Examples

#'
set.seed(110)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 2
muBar <- c(0, 0)
qVec <- c(1, 1)
constraint <- c(0, 0, 0)
X <- t(
  fabMix::simData(
    sameLambda = TRUE,
    sameSigma = TRUE,
    K.true = K,
    n = n,
    q = q,
    p = p,
    sINV_values = 1 / ((1:p))
  )$data
)
hparam <- new(
  "Hparam",
  alpha1 = 0.567755037123148,
  alpha2 = 1.1870201935945,
  delta = 2,
  ggamma = 2,
  bbeta = 3.39466184520673
)
ZOneDim <- sample(seq_len(m), n, replace = TRUE)
thetaYList <-
new("ThetaYList", tao = c(0.90162050961987, 0.0983794903801295),
psy = list(structure(c(3.68472841602225, 0, 0, 8.34691978354054),
.Dim = c(2L, 2L)), structure(c(0.785011896130842, 0, 0, 1.19022383323437),
.Dim = c(2L, 2L))), M = list(structure(c(
  2.96424305287004,
  1.08454861414306
), .Dim = 1:2), structure(c(
  -0.232625450433964,
  0.984505960868685
), .Dim = 1:2)), lambda = list(structure(c(
  -0.964026624054337,
  0.89378616732449
), .Dim = 2:1), structure(c(
  0.533334148228635,
  -1.80033696090263
), .Dim = 2:1)), Y = list(structure(c(
  -0.15346475266988,
  1.6584112693271, 0.409294936277862, -1.46628591247549, -0.532753243163142,
  -0.332143130316749, 0.307558110800446, -0.525374243612587, 0.527667526535661,
  0.748193650431916
), .Dim = c(1L, 10L)), structure(c(
  0.571325118638535,
  0.542462985882966, 0.559971315637159, -1.73905343105432, -0.583549598471542,
  1.71264245945391, -0.327119395945831, 1.02464651767821, -1.11462280255215,
  0.81095592501554
), .Dim = c(1L, 10L))))
qnew <- 1
dVec <- c(1, 1, 1)
sVec <- c(1, 1, 1)
constraint <- c(0, 0, 0)
clusInd <- rep(1, m)

stayMCMCupdate(
  X,
  thetaYList,
  ZOneDim,
  hparam,
  qVec,
  qnew,
  dVec,
  sVec,
  constraint,
  clusInd
)

bpgmm documentation built on June 2, 2022, 1:10 a.m.