Package 'bpgmm' reference manual

Title:	Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models
Description:	Model-based clustering using Bayesian parsimonious Gaussian mixture models. MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.
Authors:	Xiang Lu <Xiang_Lu at urmc.rochester.edu>, Yaoxiang Li <yl814 at georgetown.edu>, Tanzy Love <tanzy_love at urmc.rochester.edu>
Maintainer:	Yaoxiang Li <[email protected]>
License:	GPL-3
Version:	1.0.9
Built:	2024-11-12 03:56:09 UTC
Source:	https://github.com/cran/bpgmm

CalculateProposalLambda

Description

CalculateProposalLambda

Usage

CalculateProposalLambda(hparam, thetaYList, CxyList, constraint, m, p,
  qVec)
CalculateProposalLambda(hparam, thetaYList, CxyList, constraint, m, p,
  qVec)

Arguments

`hparam`	hparam
`thetaYList`	thetaYList
`CxyList`	CxyList
`constraint`	constraint
`m`	the number of clusters
`p`	the number of features
`qVec`	the vector of the number of factors in each clusters

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
X <- t(
  fabMix::simData(
    sameLambda = TRUE,
    sameSigma = TRUE,
    K.true = K,
    n = n,
    q = q,
    p = p,
    sINV_values = 1 / ((1:p))
  )$data
)
m <- 1
muBar <- c(0, 0)

hparam <- new(
  "Hparam",
  alpha1 = 0.567755037123148,
  alpha2 = 1.1870201935945,
  delta = 2,
  ggamma = 2,
  bbeta = 3.39466184520673
)
qVec <- c(1, 1)
constraint <- c(0, 0, 0)
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)
    ))
  )
CxyList <-
  list(
    A = list(structure(
      c(0.567755037123148, 0, 0, 1.1870201935945),
      .Dim = c(2L, 2L)
    )),
    nVec = structure(10, .Dim = c(1L, 1L)),
    Cxxk = list(structure(
      c(
        739.129405647622,
        671.040583460732,
        671.040583460732,
        618.754338945564
      ),
      .Dim = c(2L, 2L)
    )),
    Cxyk = list(structure(
      c(-18.5170828875512, -16.5748393456787),
      .Dim = 2:1
    )),
    Cyyk = list(structure(2.4786991560888, .Dim = c(
      1L,
      1L
    ))),
    Cytytk = list(structure(
      c(
        10, 0.787438922114998, 0.787438922114998,
        2.4786991560888
      ),
      .Dim = c(2L, 2L)
    )),
    Cxtytk = list(structure(
      c(
        -57.5402230447872,
        -54.6677145995824,
        -18.5170828875512,
        -16.5748393456787
      ),
      .Dim = c(
        2L,
        2L
      )
    )),
    CxL1k = list(structure(
      c(-59.5168204264758, -54.6093504204781),
      .Dim = 2:1
    )),
    Cxmyk = list(structure(
      c(
        -21.0952527723962,
        -14.6807011202188
      ),
      .Dim = 2:1
    )),
    sumCxmyk = structure(c(
      -21.0952527723962,
      -14.6807011202188
    ), .Dim = 2:1),
    sumCyyk = structure(3.6657193496833, .Dim = c(
      1L,
      1L
    ))
  )

CalculateProposalLambda(hparam, thetaYList, CxyList, constraint, m, p, qVec)


set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
X <- t(
  fabMix::simData(
    sameLambda = TRUE,
    sameSigma = TRUE,
    K.true = K,
    n = n,
    q = q,
    p = p,
    sINV_values = 1 / ((1:p))
  )$data
)
m <- 1
muBar <- c(0, 0)

hparam <- new(
  "Hparam",
  alpha1 = 0.567755037123148,
  alpha2 = 1.1870201935945,
  delta = 2,
  ggamma = 2,
  bbeta = 3.39466184520673
)
qVec <- c(1, 1)
constraint <- c(0, 0, 0)
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)
    ))
  )
CxyList <-
  list(
    A = list(structure(
      c(0.567755037123148, 0, 0, 1.1870201935945),
      .Dim = c(2L, 2L)
    )),
    nVec = structure(10, .Dim = c(1L, 1L)),
    Cxxk = list(structure(
      c(
        739.129405647622,
        671.040583460732,
        671.040583460732,
        618.754338945564
      ),
      .Dim = c(2L, 2L)
    )),
    Cxyk = list(structure(
      c(-18.5170828875512, -16.5748393456787),
      .Dim = 2:1
    )),
    Cyyk = list(structure(2.4786991560888, .Dim = c(
      1L,
      1L
    ))),
    Cytytk = list(structure(
      c(
        10, 0.787438922114998, 0.787438922114998,
        2.4786991560888
      ),
      .Dim = c(2L, 2L)
    )),
    Cxtytk = list(structure(
      c(
        -57.5402230447872,
        -54.6677145995824,
        -18.5170828875512,
        -16.5748393456787
      ),
      .Dim = c(
        2L,
        2L
      )
    )),
    CxL1k = list(structure(
      c(-59.5168204264758, -54.6093504204781),
      .Dim = 2:1
    )),
    Cxmyk = list(structure(
      c(
        -21.0952527723962,
        -14.6807011202188
      ),
      .Dim = 2:1
    )),
    sumCxmyk = structure(c(
      -21.0952527723962,
      -14.6807011202188
    ), .Dim = 2:1),
    sumCyyk = structure(3.6657193496833, .Dim = c(
      1L,
      1L
    ))
  )

CalculateProposalLambda(hparam, thetaYList, CxyList, constraint, m, p, qVec)

CalculateProposalPsy

Description

CalculateProposalPsy

Usage

CalculateProposalPsy(hparam, thetaYList, CxyList, constraint, m, p, qVec)
CalculateProposalPsy(hparam, thetaYList, CxyList, constraint, m, p, qVec)

Arguments

`hparam`	hparam
`thetaYList`	thetaYList
`CxyList`	CxyList
`constraint`	constraint
`m`	the number of clusters
`p`	the number of features
`qVec`	the vector of the number of factors in each clusters

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
constraint <- c(0, 0, 0)
CxyList <-
  list(
    A = list(structure(
      c(0.567755037123148, 0, 0, 1.1870201935945),
      .Dim = c(2L, 2L)
    )),
    nVec = structure(10, .Dim = c(1L, 1L)),
    Cxxk = list(structure(
      c(
        739.129405647622,
        671.040583460732,
        671.040583460732,
        618.754338945564
      ),
      .Dim = c(2L, 2L)
    )),
    Cxyk = list(structure(
      c(-18.5170828875512, -16.5748393456787),
      .Dim = 2:1
    )),
    Cyyk = list(structure(2.4786991560888, .Dim = c(
      1L,
      1L
    ))),
    Cytytk = list(structure(
      c(
        10, 0.787438922114998, 0.787438922114998,
        2.4786991560888
      ),
      .Dim = c(2L, 2L)
    )),
    Cxtytk = list(structure(
      c(
        -57.5402230447872,
        -54.6677145995824,
        -18.5170828875512,
        -16.5748393456787
      ),
      .Dim = c(
        2L,
        2L
      )
    )),
    CxL1k = list(structure(
      c(-59.5168204264758, -54.6093504204781),
      .Dim = 2:1
    )),
    Cxmyk = list(structure(
      c(
        -21.0952527723962,
        -14.6807011202188
      ),
      .Dim = 2:1
    )),
    sumCxmyk = structure(c(
      -21.0952527723962,
      -14.6807011202188
    ), .Dim = 2:1),
    sumCyyk = structure(3.6657193496833, .Dim = c(
      1L,
      1L
    ))
  )
#'

CalculateProposalPsy(hparam, thetaYList, CxyList, constraint, m, p, qVec)

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
constraint <- c(0, 0, 0)
CxyList <-
  list(
    A = list(structure(
      c(0.567755037123148, 0, 0, 1.1870201935945),
      .Dim = c(2L, 2L)
    )),
    nVec = structure(10, .Dim = c(1L, 1L)),
    Cxxk = list(structure(
      c(
        739.129405647622,
        671.040583460732,
        671.040583460732,
        618.754338945564
      ),
      .Dim = c(2L, 2L)
    )),
    Cxyk = list(structure(
      c(-18.5170828875512, -16.5748393456787),
      .Dim = 2:1
    )),
    Cyyk = list(structure(2.4786991560888, .Dim = c(
      1L,
      1L
    ))),
    Cytytk = list(structure(
      c(
        10, 0.787438922114998, 0.787438922114998,
        2.4786991560888
      ),
      .Dim = c(2L, 2L)
    )),
    Cxtytk = list(structure(
      c(
        -57.5402230447872,
        -54.6677145995824,
        -18.5170828875512,
        -16.5748393456787
      ),
      .Dim = c(
        2L,
        2L
      )
    )),
    CxL1k = list(structure(
      c(-59.5168204264758, -54.6093504204781),
      .Dim = 2:1
    )),
    Cxmyk = list(structure(
      c(
        -21.0952527723962,
        -14.6807011202188
      ),
      .Dim = 2:1
    )),
    sumCxmyk = structure(c(
      -21.0952527723962,
      -14.6807011202188
    ), .Dim = 2:1),
    sumCyyk = structure(3.6657193496833, .Dim = c(
      1L,
      1L
    ))
  )
#'

CalculateProposalPsy(hparam, thetaYList, CxyList, constraint, m, p, qVec)

Log scale ratio calculation

Description

Log scale ratio calculation

Usage

calculateRatio(deno, nume)
calculateRatio(deno, nume)

Arguments

`deno`	denominator.
`nume`	numerator.

Value

result of ratio

Examples

deno <- log(1)
nume <- log(2)
#'

calculateRatio(deno, nume)

deno <- log(1)
nume <- log(2)
#'

calculateRatio(deno, nume)

calculateVarList

Description

calculateVarList

Usage

calculateVarList(psyList, lambdaList)
calculateVarList(psyList, lambdaList)

Arguments

`psyList`	psyList
`lambdaList`	lambdaList

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
#'

calculateVarList(thetaYList@psy, thetaYList@lambda)

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
#'

calculateVarList(thetaYList@psy, thetaYList@lambda)

changeConstraintFormat

Description

changeConstraintFormat

Usage

changeConstraintFormat(strNum)
changeConstraintFormat(strNum)

Arguments

strNum

strNum

Examples

#'

changeConstraintFormat(c(0, 0, 0))

#'

changeConstraintFormat(c(0, 0, 0))

clearCurrentThetaYlist

Description

clearCurrentThetaYlist

Usage

clearCurrentThetaYlist(thetaYList, clusInd, mMax)
clearCurrentThetaYlist(thetaYList, clusInd, mMax)

Arguments

`thetaYList`	thetaYList
`clusInd`	clusInd
`mMax`	mMax

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
clusInd <- rep(1, m)
mMax <- 1
#'

clearCurrentThetaYlist(thetaYList, clusInd, mMax)

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
clusInd <- rep(1, m)
mMax <- 1
#'

clearCurrentThetaYlist(thetaYList, clusInd, mMax)

combineClusterPara

Description

combineClusterPara

Usage

combineClusterPara(oldList, newList, ind)
combineClusterPara(oldList, newList, ind)

Arguments

`oldList`	oldList
`newList`	newList
`ind`	ind

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
newList <- oldList <-
  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)
    ))
  )
#'

combineClusterPara(oldList, newList, 1)

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
newList <- oldList <-
  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)
    ))
  )
#'

combineClusterPara(oldList, newList, 1)

evaluate Prior

Description

evaluate prior value for parameter Theta and Y.

Usage

evaluatePrior(m, p, muBar, hparam, thetaYList, ZOneDim, qVec, constraint,
  clusInd)
evaluatePrior(m, p, muBar, hparam, thetaYList, ZOneDim, qVec, constraint,
  clusInd)

Arguments

`m`	m
`p`	p
`muBar`	mu_bar
`hparam`	hyper parameter class
`thetaYList`	theta Y list
`ZOneDim`	one dim of z
`qVec`	q vector
`constraint`	type of constraint
`clusInd`	cluster indicator vector

Examples

m <- 20
n <- 500
p <- 10
muBar <- c(
  -33.1342706763595, -35.2699639183419, 48.276928009445, 16.2370659601218,
  19.0023163870536, -23.4900965314972, 37.1081269873873, 4.74944562930846,
  4.6918997353449, -4.55088073255655
)
hparam <- new("Hparam",
  alpha1 = 0.567755037123148, alpha2 = 1.1870201935945,
  delta = 2, ggamma = 2, bbeta = 3.39466184520673
)
qVec <- c(4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4)
ZOneDim <- sample(seq_len(m), n, replace = TRUE)
constraint <- c(0, 0, 0)
thetaYList <- generatePriorThetaY(m, n, p, muBar, hparam, qVec, ZOneDim, constraint)
clusInd <- rep(1, m)
#'

evaluatePrior(
  m,
  p,
  muBar,
  hparam,
  thetaYList,
  ZOneDim,
  qVec,
  constraint,
  clusInd
)

m <- 20
n <- 500
p <- 10
muBar <- c(
  -33.1342706763595, -35.2699639183419, 48.276928009445, 16.2370659601218,
  19.0023163870536, -23.4900965314972, 37.1081269873873, 4.74944562930846,
  4.6918997353449, -4.55088073255655
)
hparam <- new("Hparam",
  alpha1 = 0.567755037123148, alpha2 = 1.1870201935945,
  delta = 2, ggamma = 2, bbeta = 3.39466184520673
)
qVec <- c(4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4)
ZOneDim <- sample(seq_len(m), n, replace = TRUE)
constraint <- c(0, 0, 0)
thetaYList <- generatePriorThetaY(m, n, p, muBar, hparam, qVec, ZOneDim, constraint)
clusInd <- rep(1, m)
#'

evaluatePrior(
  m,
  p,
  muBar,
  hparam,
  thetaYList,
  ZOneDim,
  qVec,
  constraint,
  clusInd
)

evaluatePriorLambda

Description

evaluate prior value for parameter Lambda

Usage

evaluatePriorLambda(p, m, alpha2, qVec, psy, lambda, constraint, clusInd)
evaluatePriorLambda(p, m, alpha2, qVec, psy, lambda, constraint, clusInd)

Arguments

`p`	the number of features
`m`	the number of clusters
`alpha2`	hyper parameter
`qVec`	the vector of the number of factors in each clusters
`psy`	parameter
`lambda`	parameter
`constraint`	the pgmm constraint, a vector of length three with binary entry. For example, c(1,1,1) means the fully constraint model
`clusInd`	cluster indicator vector

Examples

p <- 10
m <- 20
alpha2 <- 1.18
qVec <- rep(4, m)
delta <- 2
bbeta <- 2
constraint <- c(0, 0, 0)
psy <- generatePriorPsi(
  p,
  m,
  delta,
  bbeta,
  constraint
)
lambda <- generatePriorLambda(
  p,
  m,
  alpha2,
  qVec,
  psy,
  constraint
)
clusInd <- rep(1, m)
#'

evaluatePriorLambda(
  p,
  m,
  alpha2,
  qVec,
  psy,
  lambda,
  constraint,
  clusInd
)

p <- 10
m <- 20
alpha2 <- 1.18
qVec <- rep(4, m)
delta <- 2
bbeta <- 2
constraint <- c(0, 0, 0)
psy <- generatePriorPsi(
  p,
  m,
  delta,
  bbeta,
  constraint
)
lambda <- generatePriorLambda(
  p,
  m,
  alpha2,
  qVec,
  psy,
  constraint
)
clusInd <- rep(1, m)
#'

evaluatePriorLambda(
  p,
  m,
  alpha2,
  qVec,
  psy,
  lambda,
  constraint,
  clusInd
)

evaluatePriorPsi

Description

evaluate prior value for parameter Psi

Usage

evaluatePriorPsi(psy, p, m, delta, bbeta, constraint, clusInd)
evaluatePriorPsi(psy, p, m, delta, bbeta, constraint, clusInd)

Arguments

`psy`	parameter
`p`	the number of features
`m`	the number of clusters
`delta`	parameter
`bbeta`	parameter
`constraint`	parameter
`clusInd`	cluster indicator vector

Examples

p <- 10
m <- 20
delta <- 2
bbeta <- 2
constraint <- c(0, 0, 0)
psy <- generatePriorPsi(
  p,
  m,
  delta,
  bbeta,
  constraint
)
clusInd <- rep(1, m)
#'

evaluatePriorPsi(
  psy,
  p,
  m,
  delta,
  bbeta,
  constraint,
  clusInd
)

p <- 10
m <- 20
delta <- 2
bbeta <- 2
constraint <- c(0, 0, 0)
psy <- generatePriorPsi(
  p,
  m,
  delta,
  bbeta,
  constraint
)
clusInd <- rep(1, m)
#'

evaluatePriorPsi(
  psy,
  p,
  m,
  delta,
  bbeta,
  constraint,
  clusInd
)

EvaluateProposalLambda

Description

EvaluateProposalLambda

Usage

EvaluateProposalLambda(hparam, thetaYList, CxyList, constraint, newlambda,
  m, qVec, p)
EvaluateProposalLambda(hparam, thetaYList, CxyList, constraint, newlambda,
  m, qVec, p)

Arguments

`hparam`	hparam
`thetaYList`	thetaYList
`CxyList`	CxyList
`constraint`	constraint
`newlambda`	newlambda
`m`	the number of clusters
`qVec`	the vector of the number of factors in each clusters
`p`	the number of features

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
CxyList <-
  list(
    A = list(structure(
      c(0.567755037123148, 0, 0, 1.1870201935945),
      .Dim = c(2L, 2L)
    )),
    nVec = structure(10, .Dim = c(1L, 1L)),
    Cxxk = list(structure(
      c(
        739.129405647622,
        671.040583460732,
        671.040583460732,
        618.754338945564
      ),
      .Dim = c(2L, 2L)
    )),
    Cxyk = list(structure(
      c(-18.5170828875512, -16.5748393456787),
      .Dim = 2:1
    )),
    Cyyk = list(structure(2.4786991560888, .Dim = c(
      1L,
      1L
    ))),
    Cytytk = list(structure(
      c(
        10, 0.787438922114998, 0.787438922114998,
        2.4786991560888
      ),
      .Dim = c(2L, 2L)
    )),
    Cxtytk = list(structure(
      c(
        -57.5402230447872,
        -54.6677145995824,
        -18.5170828875512,
        -16.5748393456787
      ),
      .Dim = c(
        2L,
        2L
      )
    )),
    CxL1k = list(structure(
      c(-59.5168204264758, -54.6093504204781),
      .Dim = 2:1
    )),
    Cxmyk = list(structure(
      c(
        -21.0952527723962,
        -14.6807011202188
      ),
      .Dim = 2:1
    )),
    sumCxmyk = structure(c(
      -21.0952527723962,
      -14.6807011202188
    ), .Dim = 2:1),
    sumCyyk = structure(3.6657193496833, .Dim = c(
      1L,
      1L
    ))
  )
#'

EvaluateProposalLambda(hparam, thetaYList, CxyList, constraint, thetaYList@lambda, m, qVec, p)

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
CxyList <-
  list(
    A = list(structure(
      c(0.567755037123148, 0, 0, 1.1870201935945),
      .Dim = c(2L, 2L)
    )),
    nVec = structure(10, .Dim = c(1L, 1L)),
    Cxxk = list(structure(
      c(
        739.129405647622,
        671.040583460732,
        671.040583460732,
        618.754338945564
      ),
      .Dim = c(2L, 2L)
    )),
    Cxyk = list(structure(
      c(-18.5170828875512, -16.5748393456787),
      .Dim = 2:1
    )),
    Cyyk = list(structure(2.4786991560888, .Dim = c(
      1L,
      1L
    ))),
    Cytytk = list(structure(
      c(
        10, 0.787438922114998, 0.787438922114998,
        2.4786991560888
      ),
      .Dim = c(2L, 2L)
    )),
    Cxtytk = list(structure(
      c(
        -57.5402230447872,
        -54.6677145995824,
        -18.5170828875512,
        -16.5748393456787
      ),
      .Dim = c(
        2L,
        2L
      )
    )),
    CxL1k = list(structure(
      c(-59.5168204264758, -54.6093504204781),
      .Dim = 2:1
    )),
    Cxmyk = list(structure(
      c(
        -21.0952527723962,
        -14.6807011202188
      ),
      .Dim = 2:1
    )),
    sumCxmyk = structure(c(
      -21.0952527723962,
      -14.6807011202188
    ), .Dim = 2:1),
    sumCyyk = structure(3.6657193496833, .Dim = c(
      1L,
      1L
    ))
  )
#'

EvaluateProposalLambda(hparam, thetaYList, CxyList, constraint, thetaYList@lambda, m, qVec, p)

EvaluateProposalPsy

Description

EvaluateProposalPsy

Usage

EvaluateProposalPsy(hparam, thetaYList, CxyList, constraint, newpsy, m, p,
  qVec, delta)
EvaluateProposalPsy(hparam, thetaYList, CxyList, constraint, newpsy, m, p,
  qVec, delta)

Arguments

`hparam`	hparam
`thetaYList`	thetaYList
`CxyList`	CxyList
`constraint`	constraint
`newpsy`	newpsy
`m`	the number of clusters
`p`	the number of features
`qVec`	the vector of the number of factors in each clusters
`delta`	hyperparameters

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
constraint <- c(0, 0, 0)
CxyList <-
  list(
    A = list(structure(
      c(0.567755037123148, 0, 0, 1.1870201935945),
      .Dim = c(2L, 2L)
    )),
    nVec = structure(10, .Dim = c(1L, 1L)),
    Cxxk = list(structure(
      c(
        739.129405647622,
        671.040583460732,
        671.040583460732,
        618.754338945564
      ),
      .Dim = c(2L, 2L)
    )),
    Cxyk = list(structure(
      c(-18.5170828875512, -16.5748393456787),
      .Dim = 2:1
    )),
    Cyyk = list(structure(2.4786991560888, .Dim = c(
      1L,
      1L
    ))),
    Cytytk = list(structure(
      c(
        10, 0.787438922114998, 0.787438922114998,
        2.4786991560888
      ),
      .Dim = c(2L, 2L)
    )),
    Cxtytk = list(structure(
      c(
        -57.5402230447872,
        -54.6677145995824,
        -18.5170828875512,
        -16.5748393456787
      ),
      .Dim = c(
        2L,
        2L
      )
    )),
    CxL1k = list(structure(
      c(-59.5168204264758, -54.6093504204781),
      .Dim = 2:1
    )),
    Cxmyk = list(structure(
      c(
        -21.0952527723962,
        -14.6807011202188
      ),
      .Dim = 2:1
    )),
    sumCxmyk = structure(c(
      -21.0952527723962,
      -14.6807011202188
    ), .Dim = 2:1),
    sumCyyk = structure(3.6657193496833, .Dim = c(
      1L,
      1L
    ))
  )

EvaluateProposalPsy(hparam, thetaYList, CxyList, constraint, thetaYList@psy, m, p, qVec, delta)

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
constraint <- c(0, 0, 0)
CxyList <-
  list(
    A = list(structure(
      c(0.567755037123148, 0, 0, 1.1870201935945),
      .Dim = c(2L, 2L)
    )),
    nVec = structure(10, .Dim = c(1L, 1L)),
    Cxxk = list(structure(
      c(
        739.129405647622,
        671.040583460732,
        671.040583460732,
        618.754338945564
      ),
      .Dim = c(2L, 2L)
    )),
    Cxyk = list(structure(
      c(-18.5170828875512, -16.5748393456787),
      .Dim = 2:1
    )),
    Cyyk = list(structure(2.4786991560888, .Dim = c(
      1L,
      1L
    ))),
    Cytytk = list(structure(
      c(
        10, 0.787438922114998, 0.787438922114998,
        2.4786991560888
      ),
      .Dim = c(2L, 2L)
    )),
    Cxtytk = list(structure(
      c(
        -57.5402230447872,
        -54.6677145995824,
        -18.5170828875512,
        -16.5748393456787
      ),
      .Dim = c(
        2L,
        2L
      )
    )),
    CxL1k = list(structure(
      c(-59.5168204264758, -54.6093504204781),
      .Dim = 2:1
    )),
    Cxmyk = list(structure(
      c(
        -21.0952527723962,
        -14.6807011202188
      ),
      .Dim = 2:1
    )),
    sumCxmyk = structure(c(
      -21.0952527723962,
      -14.6807011202188
    ), .Dim = 2:1),
    sumCyyk = structure(3.6657193496833, .Dim = c(
      1L,
      1L
    ))
  )

EvaluateProposalPsy(hparam, thetaYList, CxyList, constraint, thetaYList@psy, m, p, qVec, delta)

generatePriorLambda

Description

evaluate prior value for parameter Lambda

Usage

generatePriorLambda(p, m, alpha2, qVec, psy, constraint)
generatePriorLambda(p, m, alpha2, qVec, psy, constraint)

Arguments

`p`	the number of features
`m`	the number of clusters
`alpha2`	hyper parameter
`qVec`	parameter
`psy`	parameter
`constraint`	parameter

Examples

p <- 10
m <- 20
alpha2 <- 1.18
qVec <- rep(4, m)
delta <- 2
bbeta <- 2
constraint <- c(0, 0, 0)
psy <- generatePriorPsi(
  p,
  m,
  delta,
  bbeta,
  constraint
)
#'

generatePriorLambda(
  p,
  m,
  alpha2,
  qVec,
  psy,
  constraint
)

p <- 10
m <- 20
alpha2 <- 1.18
qVec <- rep(4, m)
delta <- 2
bbeta <- 2
constraint <- c(0, 0, 0)
psy <- generatePriorPsi(
  p,
  m,
  delta,
  bbeta,
  constraint
)
#'

generatePriorLambda(
  p,
  m,
  alpha2,
  qVec,
  psy,
  constraint
)

generatePriorPsi

Description

generate prior value for parameter Psi

Usage

generatePriorPsi(p, m, delta, bbeta, constraint)
generatePriorPsi(p, m, delta, bbeta, constraint)

Arguments

`p`	the number of features
`m`	the number of clusters
`delta`	hyperparameters
`bbeta`	hyperparameters
`constraint`	the pgmm constraint, a vector of length three with binary entry. For example, c(1,1,1) means the fully constraint model

Examples

p <- 10
m <- 20
delta <- 2
bbeta <- 2
constraint <- c(0, 0, 0)


generatePriorPsi(
  p,
  m,
  delta,
  bbeta,
  constraint
)

p <- 10
m <- 20
delta <- 2
bbeta <- 2
constraint <- c(0, 0, 0)


generatePriorPsi(
  p,
  m,
  delta,
  bbeta,
  constraint
)

PriorThetaY list

Description

generate prior value for parameter Theta and Y.

Usage

generatePriorThetaY(m, n, p, muBar, hparam, qVec, ZOneDim, constraint)
generatePriorThetaY(m, n, p, muBar, hparam, qVec, ZOneDim, constraint)

Arguments

`m`	the number of cluster
`n`	sample size
`p`	number of covariates
`muBar`	parameter
`hparam`	hyperparameters
`qVec`	the vector of the number of factors in each clusters
`ZOneDim`	ZOneDim
`constraint`	constraint

Examples

m <- 20
n <- 500
p <- 10
muBar <- c(
  -33.1342706763595, -35.2699639183419, 48.276928009445, 16.2370659601218,
  19.0023163870536, -23.4900965314972, 37.1081269873873, 4.74944562930846,
  4.6918997353449, -4.55088073255655
)
hparam <- new("Hparam",
  alpha1 = 0.567755037123148, alpha2 = 1.1870201935945,
  delta = 2, ggamma = 2, bbeta = 3.39466184520673
)
qVec <- c(4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4)
ZOneDim <- sample(seq_len(m), n, replace = TRUE)
constraint <- c(0, 0, 0)
#'

generatePriorThetaY(m, n, p, muBar, hparam, qVec, ZOneDim, constraint)


m <- 20
n <- 500
p <- 10
muBar <- c(
  -33.1342706763595, -35.2699639183419, 48.276928009445, 16.2370659601218,
  19.0023163870536, -23.4900965314972, 37.1081269873873, 4.74944562930846,
  4.6918997353449, -4.55088073255655
)
hparam <- new("Hparam",
  alpha1 = 0.567755037123148, alpha2 = 1.1870201935945,
  delta = 2, ggamma = 2, bbeta = 3.39466184520673
)
qVec <- c(4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4)
ZOneDim <- sample(seq_len(m), n, replace = TRUE)
constraint <- c(0, 0, 0)
#'

generatePriorThetaY(m, n, p, muBar, hparam, qVec, ZOneDim, constraint)

getIndThetaY

Description

getIndThetaY

Usage

getIndThetaY(thetaYList, Ind)
getIndThetaY(thetaYList, Ind)

Arguments

`thetaYList`	thetaYList
`Ind`	Ind

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)
    ))
  )
#'

getIndThetaY(thetaYList, 1)

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)
    ))
  )
#'

getIndThetaY(thetaYList, 1)

getmode

Description

getmode

Usage

getmode(v)
getmode(v)

Arguments

v

Examples

#'

getmode(c(1, 1, 2, 3))

#'

getmode(c(1, 1, 2, 3))

getRemovedIndThetaY

Description

getRemovedIndThetaY

Usage

getRemovedIndThetaY(thetaYList, Ind)
getRemovedIndThetaY(thetaYList, Ind)

Arguments

`thetaYList`	thetaYList
`Ind`	Ind

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 = 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))))
Ind <- 1
#'

getRemovedIndThetaY(thetaYList, Ind)

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 = 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))))
Ind <- 1
#'

getRemovedIndThetaY(thetaYList, Ind)

getThetaYWithEmpty

Description

getThetaYWithEmpty

Usage

getThetaYWithEmpty(NEthetaYList, clusInd)
getThetaYWithEmpty(NEthetaYList, clusInd)

Arguments

`NEthetaYList`	NEthetaYList
`clusInd`	clusInd

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
clusInd <- rep(1, m)

getThetaYWithEmpty(thetaYList, clusInd)


set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
clusInd <- rep(1, m)

getThetaYWithEmpty(thetaYList, clusInd)

Tool for vector to matrix

Description

Tool for vector to matrix

Usage

getZmat(ZOneDim, m, n)
getZmat(ZOneDim, m, n)

Arguments

`ZOneDim`	a vector.
`m`	the number of cluster.
`n`	sample size.

Value

adjacency matrix

Examples

m <- 20
n <- 500
ZOneDim <- sample(seq_len(m), n, replace = TRUE)
#'

getZmat(ZOneDim, m, n)

m <- 20
n <- 500
ZOneDim <- sample(seq_len(m), n, replace = TRUE)
#'

getZmat(ZOneDim, m, n)

An S4 class to represent a Hyper parameter.

Description

An S4 class to represent a Hyper parameter.

Slots

alpha1: A numeric value
alpha2: A numeric value
delta: A numeric value
ggamma: A numeric value
bbeta: A numeric value

Examples

new("Hparam", alpha1 = 1, alpha2 = 2, bbeta = 3, delta = 4, ggamma = 5)
new("Hparam", alpha1 = 1, alpha2 = 2, bbeta = 3, delta = 4, ggamma = 5)

likelihood

Description

likelihood

Usage

likelihood(thetaYList, ZOneDim, qqVec, muBar, X)
likelihood(thetaYList, ZOneDim, qqVec, muBar, X)

Arguments

`thetaYList`	thetaYList
`ZOneDim`	ZOneDim
`qqVec`	qqVec
`muBar`	muBar
`X`	X

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
#'

likelihood(thetaYList, ZOneDim, qVec, muBar, X)

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
#'

likelihood(thetaYList, ZOneDim, qVec, muBar, X)

Convert list of string to vector of string

Description

Convert list of string to vector of string

Usage

listToStrVec(stringList)
listToStrVec(stringList)

Arguments

stringList

list of string

Value

vector of string

Examples

stringList <- list("abc")
#'

listToStrVec(stringList)

stringList <- list("abc")
#'

listToStrVec(stringList)

MstepRJMCMCupdate

Description

MstepRJMCMCupdate

Usage

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

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 Model-Based Clustering Using Baysian PGMM Carries out model-based clustering using parsimonious Gaussian mixture models. MCMC are used for parameter estimation. The RJMCMC is used for model selection.

Description

bpgmm Model-Based Clustering Using Baysian PGMM Carries out model-based clustering using parsimonious Gaussian mixture models. MCMC are used for parameter estimation. The RJMCMC is used for model selection.

Usage

pgmmRJMCMC(X, mInit, mVec, qnew, delta = 2, ggamma = 2, burn = 20,
  niter = 1000, constraint = C(0, 0, 0), dVec = c(1, 1, 1),
  sVec = c(1, 1, 1), Mstep = 0, Vstep = 0, SCind = 0)
pgmmRJMCMC(X, mInit, mVec, qnew, delta = 2, ggamma = 2, burn = 20,
  niter = 1000, constraint = C(0, 0, 0), dVec = c(1, 1, 1),
  sVec = c(1, 1, 1), Mstep = 0, Vstep = 0, SCind = 0)

Arguments

`X`	the observation matrix with size p * m
`mInit`	the number of initial clusters
`mVec`	the range of the number of clusters
`qnew`	the number of factor for a new cluster
`delta`	scaler hyperparameters
`ggamma`	scaler hyperparameters
`burn`	the number of burn in iterations
`niter`	the number of iterations
`constraint`	the pgmm initial constraint, a vector of length three with binary entry. For example, c(1,1,1) means the fully constraint model
`dVec`	a vector of hyperparameters with length three, shape parameters for alpha1, alpha2 and bbeta respectively
`sVec`	sVec a vector of hyperparameters with length three, rate parameters for alpha1, alpha2 and bbeta respectively
`Mstep`	the indicator of whether do model selection on the number of clusters
`Vstep`	the indicator of whether do model selection on variance structures
`SCind`	the indicator of whether use split/combine step in Mstep

Examples



library("fabMix")
library("mclust")
library("pgmm")
library("mvtnorm")
library("mcmcse")
library("MASS")
library("gtools")
n <- 500
p <- 10
q <- 4
K <- 10
nsim <- 10
burn <- 20
qnew <- 4
Mstep <- 1
Vstep <- 1
constraint <- c(0, 0, 0)
mInit <- 20
mVec <- c(1, 20)
X <- t(simData(
  sameLambda = TRUE,
  sameSigma = TRUE,
  K.true = K, n = n, q = q, p = p, sINV_values = 1 / ((1:p))
)$data)

pgmmRJMCMC(X,
  mInit, mVec, qnew,
  niter = nsim, burn = burn,
  constraint = constraint, Mstep = Mstep, Vstep = Vstep
)


library("fabMix")
library("mclust")
library("pgmm")
library("mvtnorm")
library("mcmcse")
library("MASS")
library("gtools")
n <- 500
p <- 10
q <- 4
K <- 10
nsim <- 10
burn <- 20
qnew <- 4
Mstep <- 1
Vstep <- 1
constraint <- c(0, 0, 0)
mInit <- 20
mVec <- c(1, 20)
X <- t(simData(
  sameLambda = TRUE,
  sameSigma = TRUE,
  K.true = K, n = n, q = q, p = p, sINV_values = 1 / ((1:p))
)$data)

pgmmRJMCMC(X,
  mInit, mVec, qnew,
  niter = nsim, burn = burn,
  constraint = constraint, Mstep = Mstep, Vstep = Vstep
)

stayMCMCupdate

Description

stayMCMCupdate

Usage

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

#'
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
)

sumerizeZ

Description

sumerizeZ

Usage

sumerizeZ(Zlist, index = 1:length(Zlist))
sumerizeZ(Zlist, index = 1:length(Zlist))

Arguments

`Zlist`	Zlist
`index`	index

Examples

Zlist <- list(c(1, 2, 3), c(3, 2, 1), c(2, 2, 2))
#'

sumerizeZ(Zlist)

Zlist <- list(c(1, 2, 3), c(3, 2, 1), c(2, 2, 2))
#'

sumerizeZ(Zlist)

summerizePgmmRJMCMC

Description

summerizePgmmRJMCMC

Usage

summerizePgmmRJMCMC(pgmmResList, trueCluster = NULL)
summerizePgmmRJMCMC(pgmmResList, trueCluster = NULL)

Arguments

`pgmmResList`	result list from pgmmRJMCMC
`trueCluster`	true cluster allocation

Examples

library("fabMix")
library("mclust")
library("pgmm")
library("mvtnorm")
library("mcmcse")
library("MASS")
library("gtools")
n <- 50
p <- 10
q <- 4
K <- 10
syntheticDataset <- simData(
  sameLambda = TRUE, sameSigma = TRUE, K.true = K, n = n, q = q, p = p,
  sINV_values = 1 / ((1:p))
)
nsim <- 5
burn <- 0
X <- t(syntheticDataset$data)
qnew <- 4
Mstep <- 1
Vstep <- 1
constraint <- c(0, 0, 0)
mInit <- 20
mVec <- c(1, 20)

res <- pgmmRJMCMC(X, mInit, mVec, qnew,
  niter = nsim, burn = burn, constraint = constraint,
  Mstep = Mstep, Vstep = Vstep
)



summerizePgmmRJMCMC(res, syntheticDataset$class)

library("fabMix")
library("mclust")
library("pgmm")
library("mvtnorm")
library("mcmcse")
library("MASS")
library("gtools")
n <- 50
p <- 10
q <- 4
K <- 10
syntheticDataset <- simData(
  sameLambda = TRUE, sameSigma = TRUE, K.true = K, n = n, q = q, p = p,
  sINV_values = 1 / ((1:p))
)
nsim <- 5
burn <- 0
X <- t(syntheticDataset$data)
qnew <- 4
Mstep <- 1
Vstep <- 1
constraint <- c(0, 0, 0)
mInit <- 20
mVec <- c(1, 20)

res <- pgmmRJMCMC(X, mInit, mVec, qnew,
  niter = nsim, burn = burn, constraint = constraint,
  Mstep = Mstep, Vstep = Vstep
)



summerizePgmmRJMCMC(res, syntheticDataset$class)

ThetaYList-class

Description

Definiton of ThetaYList parameter sets

Slots

tao: A numeric vector
psy: A list value
M: A list value
lambda: A list value
Y: A list value

Title

Description

Title

Usage

toEthetaYlist(NEthetaYList, NEZOneDim, qnew, clusInd)
toEthetaYlist(NEthetaYList, NEZOneDim, qnew, clusInd)

Arguments

`NEthetaYList`	NEthetaYList
`NEZOneDim`	NEZOneDim
`qnew`	qnew
`clusInd`	clusInd

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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))
  )$data
)
hparam <- new(
  "Hparam",
  alpha1 = 0.567755037123148,
  alpha2 = 1.1870201935945,
  delta = 2,
  ggamma = 2,
  bbeta = 3.39466184520673
)
ZOneDim <- sample(seq_len(m), n, replace = TRUE)
thetaYList <-
  new(
    "ThetaYList",
    tao = 0.366618687752634,
    psy = list(structure(
      c(
        4.18375613018654,
        0, 0, 5.46215996830771
      ),
      .Dim = c(2L, 2L)
    )),
    M = list(structure(
      c(
        3.27412045866392,
        -2.40544145363349
      ),
      .Dim = 1:2
    )),
    lambda = list(structure(
      c(
        2.51015961514781,
        -0.0741189919182549
      ),
      .Dim = 2:1
    )),
    Y = list(structure(
      c(
        -0.244239011725104,
        -0.26876172736886,
        0.193431511203083,
        0.41624466812811,
        -0.54581548068437,
        -0.0479517628308146,
        -0.633383997203325,
        0.856855296613208,
        0.792850576988512,
        0.268208848994559
      ),
      .Dim = c(1L, 10L)
    ))
  )
clusInd <- rep(1, m)
qnew <- 1
toEthetaYlist(thetaYList, ZOneDim, qnew, clusInd)
set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
muBar <- c(0, 0)
qVec <- c(1, 1)
constraint <- c(0, 0, 0)
X <- t(
  fabMix::simData(
    sameLambda = TRUE,
    sameSigma = TRUE,
    K.true = K,
    n = n,
    q = q,
    p = p,
    sINV_values = 1 / ((1:p))
  )$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)
    ))
  )
clusInd <- rep(1, m)
qnew <- 1
toEthetaYlist(thetaYList, ZOneDim, qnew, clusInd)

toNEthetaYlist

Description

toNEthetaYlist

Usage

toNEthetaYlist(thetaYList, ZOneDim, qVec, clusInd)
toNEthetaYlist(thetaYList, ZOneDim, qVec, clusInd)

Arguments

`thetaYList`	thetaYList
`ZOneDim`	ZOneDim
`qVec`	qVec
`clusInd`	clusInd

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
clusInd <- rep(1, m)

toNEthetaYlist(thetaYList, ZOneDim, qVec, clusInd)


set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
clusInd <- rep(1, m)

toNEthetaYlist(thetaYList, ZOneDim, qVec, clusInd)

Update posterior theta Y list

Description

Update posterior theta Y list

Usage

updatePostThetaY(m, n, p, hparam, thetaYList, ZOneDim, qVec, constraint, X,
  ggamma)
updatePostThetaY(m, n, p, hparam, thetaYList, ZOneDim, qVec, constraint, X,
  ggamma)

Arguments

`m`	the number of clusters.
`n`	the number of observations.
`p`	the number of variables
`hparam`	hyper parameters
`thetaYList`	theta Y list
`ZOneDim`	ZOneDim
`qVec`	qVec
`constraint`	constraint
`X`	X
`ggamma`	ggamma

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
constraint <- c(0, 0, 0)
#'

updatePostThetaY(m, n, p, hparam, thetaYList, ZOneDim, qVec, constraint, X, ggamma)

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )
constraint <- c(0, 0, 0)
#'

updatePostThetaY(m, n, p, hparam, thetaYList, ZOneDim, qVec, constraint, X, ggamma)

updatePostZ

Description

updatePostZ

Usage

updatePostZ(X, m, n, thetaYList)
updatePostZ(X, m, n, thetaYList)

Arguments

`X`	X
`m`	m
`n`	n
`thetaYList`	thetaYList

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )

updatePostZ(X, m, n, thetaYList)

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)
    ))
  )

updatePostZ(X, m, n, thetaYList)

VstepRJMCMCupdate

Description

VstepRJMCMCupdate

Usage

VstepRJMCMCupdate(X, muBar, p, thetaYList, ZOneDim, hparam, hparamInit,
  qVec, qnew, ggamma, dVec, sVec, constraint, clusInd)
VstepRJMCMCupdate(X, muBar, p, thetaYList, ZOneDim, hparam, hparamInit,
  qVec, qnew, ggamma, dVec, sVec, constraint, clusInd)

Arguments

`X`	X
`muBar`	muBar
`p`	p
`thetaYList`	thetaYList
`ZOneDim`	ZOneDim
`hparam`	hparam
`hparamInit`	hparamInit
`qVec`	qVec
`qnew`	qnew
`ggamma`	ggamma
`dVec`	dVec
`sVec`	sVec
`constraint`	constraint
`clusInd`	clusInd

Examples

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)

VstepRJMCMCupdate(
  X,
  muBar,
  p,
  thetaYList,
  ZOneDim,
  hparam,
  hparamInit,
  qVec,
  qnew,
  ggamma,
  dVec,
  sVec,
  constraint,
  clusInd
)

set.seed(100)
n <- 10
p <- 2
q <- 1
K <- 2
m <- 1
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)

VstepRJMCMCupdate(
  X,
  muBar,
  p,
  thetaYList,
  ZOneDim,
  hparam,
  hparamInit,
  qVec,
  qnew,
  ggamma,
  dVec,
  sVec,
  constraint,
  clusInd
)

Package 'bpgmm'

Help Index

CalculateProposalLambda

Description

Usage

Arguments

Examples

CalculateProposalPsy

Description

Usage

Arguments

Examples

Log scale ratio calculation

Description

Usage

Arguments

Value

Examples

calculateVarList

Description

Usage

Arguments

Examples

changeConstraintFormat

Description

Usage

Arguments

Examples

clearCurrentThetaYlist

Description

Usage

Arguments

Examples

combineClusterPara

Description

Usage

Arguments

Examples

evaluate Prior

Description

Usage

Arguments

Examples

evaluatePriorLambda

Description

Usage

Arguments

Examples

evaluatePriorPsi

Description

Usage

Arguments

Examples

EvaluateProposalLambda

Description

Usage

Arguments

Examples

EvaluateProposalPsy

Description

Usage

Arguments

Examples

generatePriorLambda

Description

Usage

Arguments

Examples

generatePriorPsi

Description

Usage

Arguments

Examples

PriorThetaY list

Description

Usage

Arguments

Examples

getIndThetaY

Description