Package 'keyATM' reference manual

Title:	Keyword Assisted Topic Models
Description:	Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2024) <doi:10.1111/ajps.12779>.
Authors:	Shusei Eshima [aut, cre] , Tomoya Sasaki [aut], Kosuke Imai [aut], Chung-hong Chan [ctb] , Romain François [ctb] , William Lowe [ctb], Seo-young Silvia Kim [ctb]
Maintainer:	Shusei Eshima <[email protected]>
License:	GPL-3
Version:	0.5.2
Built:	2025-03-12 05:17:33 UTC
Source:	https://github.com/keyatm/keyatm

Keyword Assisted Topic Models

Description

The implementation of keyATM models.

Author(s)

Maintainer: Shusei Eshima [email protected] (ORCID)

Authors:

Tomoya Sasaki [email protected]
Kosuke Imai [email protected]

Other contributors:

Chung-hong Chan [email protected] (ORCID) [contributor]
Romain François (ORCID) [contributor]
William Lowe [email protected] [contributor]
Seo-young Silvia Kim [email protected] (ORCID) [contributor]

Estimate document-topic distribution by strata (for covariate models)

Description

Estimate document-topic distribution by strata (for covariate models)

Usage

by_strata_DocTopic(x, by_var, labels, by_values = NULL, ...)
by_strata_DocTopic(x, by_var, labels, by_values = NULL, ...)

Arguments

`x`	the output from the covariate keyATM model (see `keyATM()`).
`by_var`	character. The name of the variable to use.
`labels`	character. The labels for the values specified in `by_var` (ascending order).
`by_values`	numeric. Specific values for `by_var`, ordered from small to large. If it is not specified, all values in `by_var` will be used.
`...`	other arguments passed on to the `predict.keyATM_output()` function.

Value

strata_topicword object (a list).

Estimate subsetted topic-word distribution

Description

Estimate subsetted topic-word distribution

Usage

by_strata_TopicWord(x, keyATM_docs, by)
by_strata_TopicWord(x, keyATM_docs, by)

Arguments

`x`	the output from a keyATM model (see `keyATM()`).
`keyATM_docs`	an object generated by `keyATM_read()`.
`by`	a vector whose length is the number of documents.

Value

strata_topicword object (a list).

Return covariates used in the iteration

Description

Return covariates used in the iteration

Usage

covariates_get(x)
covariates_get(x)

Arguments

`x`	the output from the covariate keyATM model (see `keyATM()`)

Show covariates information

Description

Show covariates information

Usage

covariates_info(x)
covariates_info(x)

Arguments

`x`	the output from the covariate keyATM model (see `keyATM()`).

keyATM main function

Description

Fit keyATM models.

Usage

keyATM(
  docs,
  model,
  no_keyword_topics,
  keywords = list(),
  model_settings = list(),
  priors = list(),
  options = list(),
  keep = c()
)
keyATM(
  docs,
  model,
  no_keyword_topics,
  keywords = list(),
  model_settings = list(),
  priors = list(),
  options = list(),
  keep = c()
)

Arguments

`docs`	texts read via `keyATM_read()`.
`model`	keyATM model: `base`, `covariates`, and `dynamic`.
`no_keyword_topics`	the number of regular topics.
`keywords`	a list of keywords.
`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 seed: A numeric value for random seed. If it is not provided, the package randomly selects a seed. iterations: An integer. Number of iterations. Default is `1500`. verbose: If `TRUE`, it prints loglikelihood and perplexity. Default is `FALSE`. llk_per: An integer. If the value is `j` keyATM stores loglikelihood and perplexity every $j$ iteration. Default value is `10` per iterations use_weights: If `TRUE` use weight. Default is `TRUE`. weights_type: There are four types of weights. Weights based on the information theory (`information-theory`) and inverse frequency (`inv-freq`) and normalized versions of them (`information-theory-normalized` and `inv-freq-normalized`). Default is `information-theory`. prune: If `TRUE` rume keywords that do not appear in the corpus. Default is `TRUE`. store_theta: If `TRUE` or `1`, it stores $\theta$ (document-topic distribution) for the iteration specified by thinning. Default is `FALSE` (same as `0`). store_pi: If `TRUE` or `1`, it stores $\pi$ (the probability of using keyword topic word distribution) for the iteration specified by thinning. Default is `FALSE` (same as `0`). thinning: An integer. If the value is `j` keyATM stores following parameters every `j` iteration. The default is `5`. theta: For all models. If `store_theta` is `TRUE` document-level topic assignment is stored (sufficient statistics to calculate document-topic distributions `theta`). alpha: For the base and dynamic models. In the base model alpha is shared across all documents whereas each state has different alpha in the dynamic model. lambda: coefficients in the covariate model. R: For the dynamic model. The state each document belongs to. P: For the dynamic model. The state transition probability. parallel_init: Parallelize processes to speed up initialization. Default is `FALSE`. Please `plan()` before use this feature. resume: The resume argument is used to save and load the intermediate results of the keyATM fitting process, allowing you to resume the fitting from a previous state. The default value is `NULL` (do not resume).
`keep`	a vector of the names of elements you want to keep in output.

Value

A keyATM_output object containing:

keyword_k: number of keyword topics
no_keyword_topics: number of no-keyword topics
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: specified keywords
model_fit: perplexity and log-likelihood
pi: estimated $\pi$ (the probability of using keyword topic word distribution) for the last iteration
values_iter: values stored during iterations
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_keywords <- keyATM_data_bills$keywords
  bills_dfm <- keyATM_data_bills$doc_dfm  # quanteda dfm object
  keyATM_docs <- keyATM_read(bills_dfm)

  # keyATM Base
  out <- keyATM(docs = keyATM_docs, model = "base",
                no_keyword_topics = 5, keywords = bills_keywords)

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

## End(Not run)

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

  # keyATM Base
  out <- keyATM(docs = keyATM_docs, model = "base",
                no_keyword_topics = 5, keywords = bills_keywords)

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

## End(Not run)

Bills data

Description

Bills data

Usage

keyATM_data_bills
keyATM_data_bills

Format

A list with following objects:

doc_dfm: A quanteda dfm object of 140 documents. The text data is a part of the Congressional Bills scraped from CONGRESS.GOV.
cov: An integer vector which takes one if the Republican proposed the bill.
keywords: A list of length 4 which contains keywords for four selected topics.
time_index: An integer vector indicating the session number of each bill.
labels: An integer vector indicating 40 labels.
labels_all: An integer vector indicating all labels.

Source

CONGRESS.GOV

Read texts

Description

Read texts and create a keyATM_docs object, which is a list of texts.

Usage

keyATM_read(
  texts,
  encoding = "UTF-8",
  check = TRUE,
  keep_docnames = FALSE,
  split = 0
)
keyATM_read(
  texts,
  encoding = "UTF-8",
  check = TRUE,
  keep_docnames = FALSE,
  split = 0
)

Arguments

`texts`	input. keyATM takes a quanteda dfm (dgCMatrix), data.frame, tibble tbl_df, or a vector of file paths.
`encoding`	character. Only used when `texts` is a vector of file paths. Default is `UTF-8`.
`check`	logical. If `TRUE`, check whether there is anything wrong with the structure of texts. Default is `TRUE`.
`keep_docnames`	logical. If `TRUE`, it keeps the document names in a quanteda dfm. Default is `FALSE`.
`split`	numeric. This option works only with a quanteda dfm. It creates a two subset of the dfm by randomly splitting each document (i.e., the total number of documents is the same between two subsets). This option specifies the split proportion. Default is `0`.

Value

a keyATM_docs object. The first element is a list whose elements are split texts. The length of the list equals to the number of documents.

Examples

## Not run: 
 # Use quanteda dfm
 keyATM_docs <- keyATM_read(texts = quanteda_dfm)

 # Use data.frame or tibble (texts should be stored in a column named `text`)
 keyATM_docs <- keyATM_read(texts = data_frame_object)
 keyATM_docs <- keyATM_read(texts = tibble_object)

 # Use a vector that stores full paths to the text files
 files <- list.files(doc_folder, pattern = "*.txt", full.names = TRUE)
 keyATM_docs <- keyATM_read(texts = files)


## End(Not run)
## Not run: 
 # Use quanteda dfm
 keyATM_docs <- keyATM_read(texts = quanteda_dfm)

 # Use data.frame or tibble (texts should be stored in a column named `text`)
 keyATM_docs <- keyATM_read(texts = data_frame_object)
 keyATM_docs <- keyATM_read(texts = tibble_object)

 # Use a vector that stores full paths to the text files
 files <- list.files(doc_folder, pattern = "*.txt", full.names = TRUE)
 keyATM_docs <- keyATM_read(texts = files)


## End(Not run)

keyATM with Collapsed Variational Bayes

Description

Experimental feature: Fit keyATM base with Collapsed Variational Bayes

Usage

keyATMvb(
  docs,
  model,
  no_keyword_topics,
  keywords = list(),
  model_settings = list(),
  vb_options = list(),
  priors = list(),
  options = list(),
  keep = list()
)
keyATMvb(
  docs,
  model,
  no_keyword_topics,
  keywords = list(),
  model_settings = list(),
  vb_options = list(),
  priors = list(),
  options = list(),
  keep = list()
)

Arguments

`docs`	texts read via `keyATM_read()`
`model`	keyATM model: `base`, `covariates`, and `dynamic`
`no_keyword_topics`	the number of regular topics
`keywords`	a list of keywords
`model_settings`	a list of model specific settings (details are in the online documentation)
`vb_options`	a list of settings for Variational Bayes convtol: the default is `1e-4` init: `mcmc` (default) or `random`
`priors`	a list of priors of parameters
`options`	a list of options same as `keyATM()`. Options are used when initialization method is `mcmc`.
`keep`	a vector of the names of elements you want to keep in output

Value

A keyATM_output object

Run multinomial regression with Polya-Gamma augmentation

Description

Run multinomial regression with Polya-Gamma augmentation. There is no need to call this function directly. The keyATM Covariate internally uses this.

Usage

multiPGreg(Y, X, num_topics, PG_params, iter = 1, store_lambda = 0)
multiPGreg(Y, X, num_topics, PG_params, iter = 1, store_lambda = 0)

Arguments

`Y`	Outcomes.
`X`	Covariates.
`num_topics`	Number of topics.
`PG_params`	Parameters used in this function.
`iter`	The default is `1`.
`store_lambda`	The default is `0`.

Show a diagnosis plot of alpha

Description

Show a diagnosis plot of alpha

Usage

plot_alpha(x, start = 0, show_topic = NULL, scales = "fixed")
plot_alpha(x, start = 0, show_topic = NULL, scales = "fixed")

Arguments

`x`	the output from a keyATM model (see `keyATM()`).
`start`	integer. The start of slice iteration. Default is `0`.
`show_topic`	a vector to specify topic indexes to show. Default is `NULL`.
`scales`	character. Control the scale of y-axis (the parameter in ggplot2::facet_wrap()): `free` adjusts y-axis for parameters. Default is `fixed`.

Value

keyATM_fig object

Show a diagnosis plot of log-likelihood and perplexity

Description

Show a diagnosis plot of log-likelihood and perplexity

Usage

plot_modelfit(x, start = 1)
plot_modelfit(x, start = 1)

Arguments

`x`	the output from a keyATM model (see `keyATM()`).
`start`	integer. The starting value of iteration to use in plot. Default is `1`.

Value

keyATM_fig object.

Show the expected proportion of the corpus belonging to each topic

Description

Show the expected proportion of the corpus belonging to each topic

Usage

plot_topicprop(
  x,
  n = 3,
  show_topic = NULL,
  show_topwords = TRUE,
  label_topic = NULL,
  order = c("proportion", "topicid"),
  xmax = NULL
)
plot_topicprop(
  x,
  n = 3,
  show_topic = NULL,
  show_topwords = TRUE,
  label_topic = NULL,
  order = c("proportion", "topicid"),
  xmax = NULL
)

Arguments

`x`	the output from a keyATM model (see `keyATM()`).
`n`	The number of top words to show. Default is `3`.
`show_topic`	an integer or a vector. Indicate topics to visualize. Default is `NULL`.
`show_topwords`	logical. Show topwords. The default is `TRUE`.
`label_topic`	a character vector. The name of the topics in the plot.
`order`	The order of topics.
`xmax`	a numeric. Indicate the max value on the x axis

Value

keyATM_fig object

Plot document-topic distribution by strata (for covariate models)

Description

Plot document-topic distribution by strata (for covariate models)

Usage

## S3 method for class 'strata_doctopic'
plot(
  x,
  show_topic = NULL,
  var_name = NULL,
  by = c("topic", "covariate"),
  ci = 0.9,
  method = c("hdi", "eti"),
  point = c("mean", "median"),
  width = 0.1,
  show_point = TRUE,
  ...
)
## S3 method for class 'strata_doctopic'
plot(
  x,
  show_topic = NULL,
  var_name = NULL,
  by = c("topic", "covariate"),
  ci = 0.9,
  method = c("hdi", "eti"),
  point = c("mean", "median"),
  width = 0.1,
  show_point = TRUE,
  ...
)

Arguments

`x`	a strata_doctopic object (see `by_strata_DocTopic()`).
`show_topic`	a vector or an integer. Indicate topics to visualize.
`var_name`	the name of the variable in the plot.
`by`	`topic` or `covariate`. Default is by `topic`.
`ci`	value of the credible interval (between 0 and 1) to be estimated. Default is `0.9` (90%).
`method`	method for computing the credible interval. The Highest Density Interval (`hdi`, default) or Equal-tailed Interval (`eti`).
`point`	method for computing the point estimate. `mean` (default) or `median`.
`width`	numeric. Width of the error bars.
`show_point`	logical. Show point estimates. The default is `TRUE`.
`...`	additional arguments not used.

Value

keyATM_fig object.

Predict topic proportions for the covariate keyATM

Description

Predict topic proportions for the covariate keyATM

Usage

## S3 method for class 'keyATM_output'
predict(
  object,
  newdata,
  transform = FALSE,
  burn_in = NULL,
  parallel = TRUE,
  posterior_mean = TRUE,
  ci = 0.9,
  method = c("hdi", "eti"),
  point = c("mean", "median"),
  label = NULL,
  raw_values = FALSE,
  ...
)
## S3 method for class 'keyATM_output'
predict(
  object,
  newdata,
  transform = FALSE,
  burn_in = NULL,
  parallel = TRUE,
  posterior_mean = TRUE,
  ci = 0.9,
  method = c("hdi", "eti"),
  point = c("mean", "median"),
  label = NULL,
  raw_values = FALSE,
  ...
)

Arguments

`object`	the keyATM_output object for the covariate model.
`newdata`	New observations which should be predicted.
`transform`	Transorm and standardize the `newdata` with the same formula and option as `model_settings` used in `keyATM()`.
`burn_in`	integer. Burn-in period. If not specified, it is the half of samples. Default is `NULL`.
`parallel`	logical. If `TRUE`, parallelization for speeding up. Default is `TRUE`. Please `plan()` before use this function.
`posterior_mean`	logical. If `TRUE`, the quantity of interest to estimate is the posterior mean. Default is `TRUE`.
`ci`	value of the credible interval (between 0 and 1) to be estimated. Default is `0.9` (90%).
`method`	method for computing the credible interval. The Highest Density Interval (`hdi`, default) or Equal-tailed Interval (`eti`).
`point`	method for computing the point estimate. `mean` (default) or `median`.
`label`	a character. Add a `label` column to the output. The default is `NULL` (do not add it).
`raw_values`	a logical. Returns raw values. The default is `FALSE`.
`...`	additional arguments not used.

Convert a quanteda dictionary to keywords

Description

This function converts or reads a dictionary object from quanteda to a named list. "Glob"-style wildcard expressions (e.g. politic*) are resolved based on the available terms in your texts.

Usage

read_keywords(file = NULL, docs = NULL, dictionary = NULL, split = TRUE, ...)
read_keywords(file = NULL, docs = NULL, dictionary = NULL, split = TRUE, ...)

Arguments

`file`	file identifier for a foreign dictionary, e.g. path to a dictionary in YAML or LIWC format
`docs`	texts read via `keyATM_read()`
`dictionary`	a quanteda dictionary object, ignore if file is not NULL
`split`	boolean, if multi-word terms be seperated, e.g. "air force" splits into "air" and "force".
`...`	additional parameters for `quanteda::dictionary()`

Value

a named list which can be used as keywords for e.g. keyATM()

Examples

## Not run: 
  library(keyATM)
  library(quanteda)
  ## using the moral foundation dictionary example from quanteda
  dictfile <- tempfile()
  download.file("http://bit.ly/37cV95h", dictfile)
  data(keyATM_data_bills)
  bills_dfm <- keyATM_data_bills$doc_dfm
  keyATM_docs <- keyATM_read(bills_dfm)
  read_keywords(file = dictfile, docs = keyATM_docs, format = "LIWC")

## End(Not run)
## Not run: 
  library(keyATM)
  library(quanteda)
  ## using the moral foundation dictionary example from quanteda
  dictfile <- tempfile()
  download.file("http://bit.ly/37cV95h", dictfile)
  data(keyATM_data_bills)
  bills_dfm <- keyATM_data_bills$doc_dfm
  keyATM_docs <- keyATM_read(bills_dfm)
  read_keywords(file = dictfile, docs = keyATM_docs, format = "LIWC")

## End(Not run)

Save a figure

Description

Save a figure

Usage

save_fig(x, filename, ...)
save_fig(x, filename, ...)

Arguments

`x`	the keyATM_fig object.
`filename`	file name to create on disk.
`...`	other arguments passed on to the ggplot2::ggsave() function.

Semantic Coherence: Mimno et al. (2011)

Description

Mimno, David et al. 2011. “Optimizing Semantic Coherence in Topic Models.” In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK.: Association for Computational Linguistics, 262–72. https://aclanthology.org/D11-1024.

Usage

semantic_coherence(x, docs, n = 10)
semantic_coherence(x, docs, n = 10)

Arguments

`x`	the output from a keyATM model (see `keyATM()`).
`docs`	texts read via `keyATM_read()`.
`n`	integer. The number terms to visualize. Default is `10`.

Details

Equation 1 of Mimno et al. 2011 adopted to keyATM.

Value

A vector of topic coherence metric calculated by each topic.

Show the top documents for each topic

Description

Show the top documents for each topic

Usage

top_docs(x, n = 10)
top_docs(x, n = 10)

Arguments

`x`	the output from a keyATM model (see `keyATM()`).
`n`	How many documents to show. Default is `10`.

Value

An n x k table of the top n documents for each topic, each number is a document index.

Show the top topics for each document

Description

Show the top topics for each document

Usage

top_topics(x, n = 2)
top_topics(x, n = 2)

Arguments

`x`	the output from a keyATM model (see `keyATM()`).
`n`	integer. The number of topics to show. Default is `2`.

Value

An n x k table of the top n topics in each document.

Show the top words for each topic

Description

If show_keyword is TRUE then words in their keyword topics are suffixed with a check mark. Words from another keyword topic are labeled with the name of that category.

Usage

top_words(x, n = 10, measure = c("probability", "lift"), show_keyword = TRUE)
top_words(x, n = 10, measure = c("probability", "lift"), show_keyword = TRUE)

Arguments

`x`	the output (see `keyATM()` and `by_strata_TopicWord()`).
`n`	integer. The number terms to visualize. Default is `10`.
`measure`	character. The way to sort the terms: `probability` (default) or `lift`.
`show_keyword`	logical. If `TRUE`, mark keywords. Default is `TRUE`.

Value

An n x k table of the top n words in each topic

Get values used to create a figure

Description

Get values used to create a figure

Usage

values_fig(x)
values_fig(x)

Arguments

`x`	the keyATM_fig object.

Visualize keywords

Description

Visualize the proportion of keywords in the documents.

Usage

visualize_keywords(docs, keywords, prune = TRUE, label_size = 3.2)
visualize_keywords(docs, keywords, prune = TRUE, label_size = 3.2)

Arguments

`docs`	a keyATM_docs object, generated by `keyATM_read()` function
`keywords`	a list of keywords
`prune`	logical. If `TRUE`, prune keywords that do not appear in `docs`. Default is `TRUE`.
`label_size`	the size of keyword labels in the output plot. Default is `3.2`.

Value

keyATM_fig object

Examples

## Not run: 
 # Prepare a keyATM_docs object
 keyATM_docs <- keyATM_read(input)

 # Keywords are in a list
 keywords <- list(Education = c("education", "child", "student"),
                  Health    = c("public", "health", "program"))

 # Visualize keywords
 keyATM_viz <- visualize_keywords(keyATM_docs, keywords)

 # View a figure
 keyATM_viz

 # Save a figure
 save_fig(keyATM_viz, filename)

## End(Not run)
## Not run: 
 # Prepare a keyATM_docs object
 keyATM_docs <- keyATM_read(input)

 # Keywords are in a list
 keywords <- list(Education = c("education", "child", "student"),
                  Health    = c("public", "health", "program"))

 # Visualize keywords
 keyATM_viz <- visualize_keywords(keyATM_docs, keywords)

 # View a figure
 keyATM_viz

 # Save a figure
 save_fig(keyATM_viz, filename)

## End(Not run)

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()
)
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)

## 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)

`x`	the output from the dynamic keyATM model (see `keyATM()`).
`show_topic`	an integer or a vector. Indicate topics to visualize. Default is `NULL`.
`time_index_label`	a vector. The label for time index. The length should be equal to the number of documents (time index provided to `keyATM()`).
`ci`	value of the credible interval (between 0 and 1) to be estimated. Default is `0.9` (90%). This is an option when calculating credible intervals (you need to set `store_theta = TRUE` in `keyATM()`).
`method`	method for computing the credible interval. The Highest Density Interval (`hdi`, default) or Equal-tailed Interval (`eti`). This is an option when calculating credible intervals (you need to set `store_theta = TRUE` in `keyATM()`).
`point`	method for computing the point estimate. `mean` (default) or `median`. This is an option when calculating credible intervals (you need to set `store_theta = TRUE` in `keyATM()`).
`xlab`	a character.
`scales`	character. Control the scale of y-axis (the parameter in ggplot2::facet_wrap()): `free` adjusts y-axis for parameters. Default is `fixed`.
`show_point`	logical. The default is `TRUE`. This is an option when calculating credible intervals.
`...`	additional arguments not used.

Package 'keyATM'

Help Index

Keyword Assisted Topic Models

Description

Author(s)

See Also

Estimate document-topic distribution by strata (for covariate models)

Description

Usage

Arguments

Value

Estimate subsetted topic-word distribution

Description

Usage

Arguments

Value

Return covariates used in the iteration

Description

Usage

Arguments

Show covariates information

Description

Usage

Arguments

keyATM main function

Description

Usage

Arguments

Value

See Also

Examples

Bills data

Description

Usage

Format

Source

Read texts

Description

Usage

Arguments

Value

Examples

keyATM with Collapsed Variational Bayes

Description

Usage

Arguments

Value

See Also

Run multinomial regression with Polya-Gamma augmentation

Description

Usage

Arguments

Show a diagnosis plot of alpha

Description

Usage

Arguments

Value

See Also

Show a diagnosis plot of log-likelihood and perplexity

Description

Usage

Arguments

Value

See Also

Show a diagnosis plot of pi

Description

Usage

Arguments

Value

See Also

Plot time trend

Description

Usage

Arguments

Value

See Also

Show the expected proportion of the corpus belonging to each topic

Description

Usage

Arguments