# update_all_y: Gibbs sampling for y in 'xCx' model In fabMix: Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components

## Description

Gibbs sampling for updating the factors y for models with same variance of errors per component.

## Usage

 1 update_all_y(x_data, mu, SigmaINV, Lambda, z) 

## Arguments

 x_data n\times p matrix with obseved data mu n\times p matrix of marginal means SigmaINV p\times p precision matrix Lambda p\times q matrix of factor loadings z Allocation vector

## Value

A matrix with generated factors

## Author(s)

Panagiotis Papastamoulis

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 library('fabMix') 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) # use the real values as input and simulate factors update_all_y(x_data = syntheticDataset$data, mu = syntheticDataset$means, SigmaINV = diag(1/diag(syntheticDataset$variance)), Lambda = syntheticDataset$factorLoadings, z = syntheticDataset\$class) 

fabMix documentation built on Feb. 20, 2020, 1:09 a.m.