Description Usage Arguments Value Author(s) Examples
Gibbs sampling for Σ^{-1} for xCC models
1 | update_SigmaINV_xCC(x_data, z, y, Lambda, mu, K, alpha_sigma, beta_sigma)
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x_data |
n\times p matrix containing the observed data |
z |
Allocation vector |
y |
n\times q matrix containing the latent factors |
Lambda |
K\times p\times q array with factor loadings |
mu |
K\times p array containing the marginal means |
K |
Number of components |
alpha_sigma |
Prior parameter alpha |
beta_sigma |
Prior parameter beta |
p\times p matrix with the common variance of errors per component Σ^{-1} = σ I_p.
Panagiotis Papastamoulis
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | 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)
# use the real values as input and update SigmaINV
update_SigmaINV_xCC(x_data = syntheticDataset$data,
z = syntheticDataset$class,
y = syntheticDataset$factors,
Lambda = syntheticDataset$factorLoadings,
mu = syntheticDataset$means,
K = K,
alpha_sigma = 0.5, beta_sigma = 0.5)
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