evaluatePriorLambda: evaluatePriorLambda

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

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
)

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