Description Usage Arguments Value See Also
View source: R/lda_acgs_lppv_R.R
Computation is based on Zhe Chen (2015)'s method
1 2 | lda_acgs_lppv_R(K, V, alpha, eta, did, wid, doc.N, max.iter = 5 * 10^3,
burn.in = 10^3, spacing = 1)
|
K |
Number of topics in the corpus |
V |
Vocabulary size |
alpha |
Hyperparameter value for θ matrix |
eta |
Smoothing parameter for the β matrix |
did |
Document ids of every word instance in each corpus document (1 X total.N vector). We assume vocabulary id starts with 1 |
wid |
Vocabulary ids of every word instance in each corpus document (1 X total.N vector). We assume vocabulary id starts with 1 |
doc.N |
Documents' word counts |
max.iter |
Maximum number of Gibbs iterations to be performed |
burn.in |
Burn-in-period for the Gibbs sampler |
spacing |
Spacing between the stored samples (to reduce correlation) |
Log Posterior Predictive Value
Other posterior predictive check (PPC) options: lda_fgs_lppv_R
,
lda_fgs_lppv
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