Help for package bpgmm

Type:

Package

Title:

Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models

Version:

1.0.9

Date:

2022-05-31

Depends:

R(≥ 3.1.0)

Imports:

methods (≥ 3.5.1), mcmcse (≥ 1.3-2), pgmm (≥ 1.2.3), mvtnorm (≥ 1.0-10), MASS (≥ 7.3-51.1), Rcpp (≥ 1.0.1), gtools (≥ 3.8.1), label.switching (≥ 1.8), fabMix (≥ 5.0), mclust (≥ 5.4.3)

Author:

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 <yl814@georgetown.edu>

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>.

SystemRequirements:

C++11

License:

GPL-3

Encoding:

UTF-8

RoxygenNote:

6.1.1

Suggests:

testthat

LinkingTo:

Rcpp, RcppArmadillo

NeedsCompilation:

yes

Packaged:

2022-06-01 03:33:44 UTC; bach

Repository:

CRAN

Date/Publication:

2022-06-01 10:20:12 UTC

CalculateProposalLambda

Description

CalculateProposalLambda

Usage

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)

CalculateProposalPsy

Description

CalculateProposalPsy

Usage

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)

EvaluateProposalLambda

Description

EvaluateProposalLambda

Usage

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)

EvaluateProposalPsy

Description

EvaluateProposalPsy

Usage

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)

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)

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

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
)

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

VstepRJMCMCupdate

Description

VstepRJMCMCupdate

Usage

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

Arguments

X

muBar

muBar

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
)

Log scale ratio calculation

Description

Log scale ratio calculation

Usage

calculateRatio(deno, nume)

Arguments

deno

denominator.

nume

numerator.

Value

result of ratio

Examples

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

calculateRatio(deno, nume)

calculateVarList

Description

calculateVarList

Usage

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)

changeConstraintFormat

Description

changeConstraintFormat

Usage

changeConstraintFormat(strNum)

Arguments

strNum

strNum

Examples

#'

changeConstraintFormat(c(0, 0, 0))

clearCurrentThetaYlist

Description

clearCurrentThetaYlist

Usage

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)

combineClusterPara

Description

combineClusterPara

Usage

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)

evaluate Prior

Description

evaluate prior value for parameter Theta and Y.

Usage

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

Arguments

m

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
)

evaluatePriorLambda

Description

evaluate prior value for parameter Lambda

Usage

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
)

evaluatePriorPsi

Description

evaluate prior value for parameter Psi

Usage

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
)

generatePriorLambda

Description

evaluate prior value for parameter Lambda

Usage

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
)

generatePriorPsi

Description

generate prior value for parameter Psi

Usage

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
)

PriorThetaY list

Description

generate prior value for parameter Theta and Y.

Usage

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)

getIndThetaY

Description

getIndThetaY

Usage

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)

getRemovedIndThetaY

Description

getRemovedIndThetaY

Usage

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)

getThetaYWithEmpty

Description

getThetaYWithEmpty

Usage

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)

Tool for vector to matrix

Description

Tool for vector to matrix

Usage

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)

getmode

Description

getmode

Usage

getmode(v)

Arguments

v

Examples

#'

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

likelihood

Description

likelihood

Usage

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

Arguments

thetaYList

thetaYList

ZOneDim

ZOneDim

qqVec

qqVec

muBar

muBar

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)

Convert list of string to vector of string

Description

Convert list of string to vector of string

Usage

listToStrVec(stringList)

Arguments

stringList

list of string

Value

vector of string

Examples

stringList <- list("abc")
#'

listToStrVec(stringList)

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)

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
)

stayMCMCupdate

Description

stayMCMCupdate

Usage

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

Arguments

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
)

sumerizeZ

Description

sumerizeZ

Usage

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)

summerizePgmmRJMCMC

Description

summerizePgmmRJMCMC

Usage

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)

Title

Description

Title

Usage

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)

toNEthetaYlist

Description

toNEthetaYlist

Usage

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)

Update posterior theta Y list

Description

Update posterior theta Y list

Usage

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

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)

updatePostZ

Description

updatePostZ

Usage

updatePostZ(X, m, n, thetaYList)

Arguments

X

m

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)