weightedLDA: Weighted LDA main function
In keyATM: Keyword Assisted Topic Models

weightedLDA

R Documentation

Weighted LDA main function

Description

Fit weighted LDA models.

Usage

weightedLDA(
  docs,
  model,
  number_of_topics,
  model_settings = list(),
  priors = list(),
  options = list(),
  keep = c()
)

Arguments

`docs`	texts read via `keyATM_read()`.
`model`	Weighted LDA model: `base`, `covariates`, and `dynamic`.
`number_of_topics`	the number of regular topics.
`model_settings`	a list of model specific settings (details are in the online documentation).
`priors`	a list of priors of parameters.
`options`	a list of options (details are in the documentation of `keyATM()`).
`keep`	a vector of the names of elements you want to keep in output.

Value

A keyATM_output object containing:

V: number of terms (number of unique words)
N: number of documents
model: the name of the model
theta: topic proportions for each document (document-topic distribution)
phi: topic specific word generation probabilities (topic-word distribution)
topic_counts: number of tokens assigned to each topic
word_counts: number of times each word type appears
doc_lens: length of each document in tokens
vocab: words in the vocabulary (a vector of unique words)
priors: priors
options: options
keywords_raw: NULL for LDA models
model_fit: perplexity and log-likelihood
pi: estimated pi for the last iteration (NULL for LDA models)
values_iter: values stored during iterations
number_of_topics: number of topics
kept_values: outputs you specified to store in keep option
information: information about the fitting

Examples

## Not run: 
  library(keyATM)
  library(quanteda)
  data(keyATM_data_bills)
  bills_dfm <- keyATM_data_bills$doc_dfm  # quanteda dfm object
  keyATM_docs <- keyATM_read(bills_dfm)

  # Weighted LDA
  out <- weightedLDA(docs = keyATM_docs, model = "base",
                     number_of_topics = 5)

  # Visit our website for full examples: https://keyatm.github.io/keyATM/

## End(Not run)

keyATM documentation built on May 31, 2023, 6:27 p.m.