force_conformance

Takes a dfm and a set of features, and makes them match the features listed
in the set.

internal

Scaling models and classifiers for sparse matrix objects representing
textual data in the form of a document-feature matrix. Includes original
implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>,
'Wordscores' model, the Perry and 'Benoit' (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model,
and the 'Slapin' and 'Proksch' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish'
model, as well as methods for correspondence analysis, latent semantic analysis,
and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.

Kenneth Benoit

quanteda.textmodels

Scaling Models and Classifiers for Textual Data

Kohei Watanabe

Haiyan Wang

Patrick O. Perry

Benjamin Lauderdale

Johannes Gruber

William Lowe

Vikas Sindhwani

European Research Council 

force_conformance function

<dl><dt>x</dt>
<dd>input dfm</dd>
<dt>features</dt>
<dd>character; a vector of feature names</dd>
<dt>force</dt>
<dd>logical; if <code>TRUE</code>, make the new dfm conform to the vector of
features, otherwise return an error message</dd></dl>

Arguments

Internal function to match a dfm features to a target set — force_conformance

<dl>

<dt>x</dt>
<dd>input dfm</dd>


<dt>features</dt>
<dd>character; a vector of feature names</dd>


<dt>force</dt>
<dd>logical; if <code>TRUE</code>, make the new dfm conform to the vector of
features, otherwise return an error message</dd>

</dl>

Internal function to match a dfm features to a target set

force_conformance: Internal function to match a dfm features to a target set

Description

Usage

Value

Arguments

Examples