Returns the corresponding element of a LDA object.
getEstimators computes the estimators for phi and theta.
getTopics(x)getAssignments(x)
getDocument_sums(x)
getDocument_expects(x)
getLog.likelihoods(x)
getParam(x)
getK(x)
getAlpha(x)
getEta(x)
getNum.iterations(x)
getEstimators(x)
[named list]
LDA object.
The estimators for phi and theta in
$$w_n^{(m)} \mid T_n^{(m)}, \bm\phi_k \sim \textsf{Discrete}(\bm\phi_k),$$
$$\bm\phi_k \sim \textsf{Dirichlet}(\eta),$$
$$T_n^{(m)} \mid \bm\theta_m \sim \textsf{Discrete}(\bm\theta_m),$$
$$\bm\theta_m \sim \textsf{Dirichlet}(\alpha)$$
are calculated referring to Griffiths and Steyvers (2004) by
$$\hat{\phi}_{k, v} = \frac{n_k^{(v)} + \eta}{n_k + V \eta},$$
$$\hat{\theta}_{m, k} = \frac{n_k^{(m)} + \alpha}{N^{(m)} + K \alpha}$$
with \(V\) is the vocabulary size, \(K\) is the number of modeled topics;
\(n_k^{(v)}\) is the count of assignments of the \(v\)-th word to
the \(k\)-th topic. Analogously, \(n_k^{(m)}\) is the count of assignments
of the \(m\)-th text to the \(k\)-th topic. \(N^{(m)}\) is the total
number of assigned tokens in text \(m\) and \(n_k\) the total number of
assigned tokens to topic \(k\).
Griffiths, Thomas L. and Mark Steyvers (2004). "Finding scientific topics". In: Proceedings of the National Academy of Sciences 101 (suppl 1), pp.5228--5235, 10.1073/pnas.0307752101.
Other getter functions:
getJob(),
getSCLOP(),
getSimilarity()