# observed.log.likelihood0_Sj_q0: Log-likelihood of the mixture model for q=0 In fabMix: Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components

## Description

Log-likelihood of the mixture model evaluated only at the alive components.

## Usage

 1 observed.log.likelihood0_Sj_q0(x_data, w, mu, Sigma, z) 

## Arguments

 x_data The observed data w Vector of mixture weights mu Vector of marginal means Sigma K\times p matrix with each row containing the diagonal of the covariance matrix of the errors per cluster z Allocation vector

## Value

Log-likelihood value

## Author(s)

Panagiotis Papastamoulis

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  library('fabMix') data(waveDataset1500) x_data <- waveDataset1500[ 1:20, -1] # data z <- waveDataset1500[ 1:20, 1] # class p <- dim(x_data)[2] q <- 2 K <- length(table(z)) # 3 classes # give some arbitrary values to the parameters: set.seed(1) w <- rep(1/K, K) mu <- array( runif(K * p), dim = c(K,p) ) Sigma <- matrix(1:K, nrow = K, ncol = p) # compute the complete.log.likelihood observed.log.likelihood0_Sj_q0(x_data = x_data, w = w, mu = mu, Sigma = Sigma, z = z) 

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