calc_lambda

Use Bayes' rule to get P(topic|token) from the estimated parameters of a
probabilistic topic model.This resulting "lambda" matrix can be used for
classifying new documents in a frequentist context and supports
<code>augment</code>.

internal

Implements an algorithm for Latent Dirichlet
Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>,
using style conventions from the 'tidyverse',
Wickham et al. (2019)<doi:10.21105/joss.01686>,
and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>.
Fitting is done via collapsed Gibbs sampling.
Also implements several novel features for LDA such as guided models and
transfer learning.

Thomas W Jones

tidylda

Latent Dirichlet Allocation Using 'tidyverse' Conventions

Tommy Jones

Brendan Knapp

Barum Park

calc_lambda function

<dl><dt>beta</dt>
<dd>a beta matrix</dd>
<dt>theta</dt>
<dd>a theta matrix</dd>
<dt>p_docs</dt>
<dd>A numeric vector of length <code>nrow(theta)</code> that is
proportional to the number of terms in each document, defaults to NULL.</dd>
<dt>correct</dt>
<dd>Logical. Do you want to set NAs or NaNs in the final result to
zero? Useful when hitting computational underflow. Defaults to <code>TRUE</code>.
Set to <code>FALSE</code> for troubleshooting or diagnostics.</dd></dl>

Arguments

Calculate a matrix whose rows represent P(topic_i|tokens) — calc_lambda

<dl>

<dt>beta</dt>
<dd>a beta matrix</dd>


<dt>theta</dt>
<dd>a theta matrix</dd>


<dt>p_docs</dt>
<dd>A numeric vector of length <code>nrow(theta)</code> that is
proportional to the number of terms in each document, defaults to NULL.</dd>


<dt>correct</dt>
<dd>Logical. Do you want to set NAs or NaNs in the final result to
zero? Useful when hitting computational underflow. Defaults to <code>TRUE</code>.
Set to <code>FALSE</code> for troubleshooting or diagnostics.</dd>

</dl>

calc_lambda: Calculate a matrix whose rows represent P(topic_i|tokens)

Description

Usage

Value

Arguments