overfittingMFA_Sj_missing_values {fabMix}

Basic MCMC sampler for the case of missing data and different error variance

Description

Gibbs sampling for fitting a mixture model of factor analyzers.

Usage

overfittingMFA_Sj_missing_values(missing_entries, x_data, originalX, 
	outputDirectory, Kmax, 
	m, thinning, burn, g, h, alpha_prior, alpha_sigma, 
	beta_sigma, start_values, q, zStart, gibbs_z, lowerTriangular)

Arguments

missing_entries

list which contains the row number (1st entry) and column indexes (subsequent entries) for every row containing missing values.

x_data

normalized data

originalX

observed raw data (only for plotting purpose)

outputDirectory

Name of the output folder

Kmax

Number of mixture components

m

Number of iterations

thinning

Thinning of chain

burn

Burn-in period

g

Prior parameter g. Default value: g = 2.

h

Prior parameter h. Default value: h = 1.

alpha_prior

Parameters of the Dirichlet prior distribution of mixture weights.

alpha_sigma

Prior parameter \alpha. Default value: \alpha = 2.

beta_sigma

Prior parameter \beta. Default value: \beta = 1.

start_values

Optional (not used)

q

Number of factors.

zStart

Optional (not used)

gibbs_z

Optional

lowerTriangular

logical value indicating whether a lower triangular parameterization should be imposed on the matrix of factor loadings (if TRUE) or not. Default: TRUE.

Value

List of files

Author(s)

Panagiotis Papastamoulis