calclift

A primarily internal function for calculating words according to the lift metric.
We expect most users will use <code>labelTopics</code> instead.

internal

The Structural Topic Model (STM) allows researchers
to estimate topic models with document-level covariates.
The package also includes tools for model selection, visualization,
and estimation of topic-covariate regressions. Methods developed in
Roberts et. al. (2014) <doi:10.1111/ajps.12103> and
Roberts et. al. (2016) <doi:10.1080/01621459.2016.1141684>. Vignette
is Roberts et. al. (2019) <doi:10.18637/jss.v091.i02>.

Brandon Stewart

Estimation of the Structural Topic Model

Margaret Roberts

Dustin Tingley

Kenneth Benoit

calclift function

<dl><dt>logbeta</dt>
<dd>a K by V matrix containing the log probabilities of seeing word v conditional on topic k</dd>
<dt>wordcounts</dt>
<dd>a V length vector indicating the number of times each word appears in the corpus.</dd></dl>

Arguments

Calculate Lift Words — calclift

<dl>

<dt>logbeta</dt>
<dd>a K by V matrix containing the log probabilities of seeing word v conditional on topic k</dd>


<dt>wordcounts</dt>
<dd>a V length vector indicating the number of times each word appears in the corpus.</dd>

</dl>

calclift: Calculate Lift Words

Description

Usage

Arguments

Details

References

See Also