MstepRJMCMCupdate: MstepRJMCMCupdate

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

MstepRJMCMCupdate

Description

MstepRJMCMCupdate

Usage

MstepRJMCMCupdate(X, muBar, p, thetaYList, ZOneDim, hparam, hparamInit,
  qVec, qnew, dVec, sVec, constraint, clusInd, mVec, Mind)

Arguments

X	X in MstepRJMCMCupdate
muBar	muBar
p	p
thetaYList	thetaYList
ZOneDim	ZOneDim
hparam	hparam
hparamInit	hparamInit
qVec	qVec
qnew	qnew
dVec	dVec
sVec	sVec
constraint	constraint
clusInd	clusInd
mVec	mVec
Mind	Mind

Examples

set.seed(100)
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 = 0.366618687752634,
    psy = list(structure(
      c(
        4.18375613018654,
        0, 0, 5.46215996830771
      ),
      .Dim = c(2L, 2L)
    )),
    M = list(structure(
      c(
        3.27412045866392,
        -2.40544145363349
      ),
      .Dim = 1:2
    )),
    lambda = list(structure(
      c(
        2.51015961514781,
        -0.0741189919182549
      ),
      .Dim = 2:1
    )),
    Y = list(structure(
      c(
        -0.244239011725104,
        -0.26876172736886,
        0.193431511203083,
        0.41624466812811,
        -0.54581548068437,
        -0.0479517628308146,
        -0.633383997203325,
        0.856855296613208,
        0.792850576988512,
        0.268208848994559
      ),
      .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)
Mind <- "BD"
mVec <- c(1, m)

MstepRJMCMCupdate(
  X,
  muBar,
  p,
  thetaYList,
  ZOneDim,
  hparam,
  hparamInit,
  qVec,
  qnew,
  dVec,
  sVec,
  constraint,
  clusInd,
  mVec,
  Mind
)

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