weightedLDA: Weighted LDA Main function

In Shusei-E/keyATM-OLD: Keyword Assisted Topic Models

Description

Run 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
priors	a list of priors of parameters
options	a list of options
keep	a vector of the names of elements you want to keep in output

Value

A keyATM_output object containing:

number_of_topics

Number of regular unseeded topics

V

Number of word types

N

Number of documents

theta

Normalized topic proportions for each document

phi

Normalized topic specific word generation probabilities

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

model_fit

Perplexity and log-likelihood

p

Estimated p

values_iter

Organized values stored during iterations

kept_values

Outputs you specified to store.

Examples

## Not run: 
  # Weighted LDA
  out <- weightedLDA(
                     keyATM_docs, model = "base", number_of_topics = 5
                    )

  # Weighted LDA Covariates
  out <- weightedLDA(
                     keyATM_docs, model = "covariates", number_of_topics = 5,
                     model_settings(covariates_data = cov_matrix)
                    )                   

  # Weighted LDA Dynamic
  out <- weightedLDA(
                     keyATM_docs, model = "dynamic", number_of_topics = 5,
                     model_settings(time_index = time_index_vec, num_states = 5)
                    )


## End(Not run)

Shusei-E/keyATM-OLD documentation built on Dec. 23, 2019, 6:42 p.m.