Description Usage Arguments Value See Also
This a R wrapper function for the C++ implementation of the full Gibbs sampler for LDA—a Markov chain on (z, β, θ).
1 2 | lda_fgs(K, V, wid, doc.N, alpha.v, eta, max.iter = 100, burn.in = 0,
spacing = 1, save.z = 0, save.beta = 0, save.theta = 0, save.lp = 0)
|
K |
Number of topics in the corpus |
V |
Vocabulary size |
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 |
alpha.v |
Hyperparameter vector for θ |
eta |
Smoothing parameter for the β matrix |
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) |
save.z |
if 0 the function does not save z samples |
save.beta |
if 0 the function does not save β samples |
save.theta |
if 0 the function does not save θ samples |
save.lp |
if 0 the function does not save computed log posterior for iterations |
the Gibbs sampling output
Other Gibbs sampling methods: get_grid_neighbors
,
lda_acgs_hs
, lda_acgs
,
lda_fgs_hs
, lda_fgs_st
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