by_strata_DocTopic

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

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>.

Shusei Eshima

keyATM

Keyword Assisted Topic Models

Tomoya Sasaki

Kosuke Imai

Chung-hong Chan

Romain François

Martin Feldkircher

William Lowe

Seo-young Silvia Kim

by_strata_DocTopic function

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

Arguments

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

<dl>

<dt>x</dt>
<dd>the output from the covariate keyATM model (see <code>keyATM()</code>).</dd>


<dt>by_var</dt>
<dd>character. The name of the variable to use.</dd>


<dt>labels</dt>
<dd>character. The labels for the values specified in <code>by_var</code> (ascending order).</dd>


<dt>by_values</dt>
<dd>numeric. Specific values for <code>by_var</code>, ordered from small to large. If it is not specified, all values in <code>by_var</code> will be used.</dd>


<dt>...</dt>
<dd>other arguments passed on to the <code>predict.keyATM_output()</code> function.</dd>

</dl>

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

Description

Usage

Value

Arguments