# update_OmegaINV_Cxx: Gibbs sampling for Omega^{-1} for Cxx model In fabMix: Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components

 update_OmegaINV_Cxx R Documentation

## Gibbs sampling for \Omega^{-1} for Cxx model

### Description

Gibbs sampling for \Omega^{-1} for Cxx model

### Usage

update_OmegaINV_Cxx(Lambda, K, g, h)


### Arguments

 Lambda Factor loadings, in the form of K\times p\times q matrix, under the restriction that all components share the factor loadings. K Number of components g Prior parameter h Prior parameter

### Value

q\times q matrix \Omega^{-1}

### Author(s)

Panagiotis Papastamoulis

### Examples

library('fabMix')
# simulate some data
n = 8                # sample size
p = 5                # number of variables
q = 2                # number of factors
K = 2                # true number of clusters
sINV_diag = 1/((1:p))    # diagonal of inverse variance of errors
set.seed(100)
syntheticDataset <- simData(sameLambda=TRUE,K.true = K, n = n, q = q, p = p,
sINV_values = sINV_diag)
SigmaINV <- array(data = 0, dim = c(K,p,p))
for(k in 1:K){
diag(SigmaINV[k,,]) <- 1/diag(syntheticDataset$variance) + rgamma(p, shape=1, rate = 1) } # Use the real values as input and simulate allocations. # Mmake sure that in this case Lambda[k,,] is the same # for all k = 1,..., K update_OmegaINV_Cxx(Lambda = syntheticDataset$factorLoadings,
K = K, g=0.5, h = 0.5)



fabMix documentation built on May 29, 2024, 2:53 a.m.