influence.predict.textmodel_affinity

Computes the influence of features on scaled textmodel_affinity
applications.

internal

textmodel

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, Perry and 'Benoit's' (2017) <arXiv:1710.08963> class affinity scaling model,
and 'Slapin' and 'Proksch's' (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 

influence.predict.textmodel_affinity function

<dl><dt>model</dt>
<dd>a predicted
textmodel_affinity object</dd>
<dt>subset</dt>
<dd>whether to use all data or a subset (for instance, exclude the
training set)</dd>
<dt>...</dt>
<dd>unused</dd></dl>

Arguments

Compute feature influence from a predicted textmodel_affinity object — influence.predict.textmodel_affinity

<dl>

<dt>model</dt>
<dd>a predicted
textmodel_affinity object</dd>


<dt>subset</dt>
<dd>whether to use all data or a subset (for instance, exclude the
training set)</dd>


<dt>...</dt>
<dd>unused</dd>

</dl>

Compute feature influence from a predicted textmodel_affinity object

influence.predict.textmodel_affinity: Compute feature influence from a predicted textmodel_affinity object

Description

Usage

Arguments

See Also

Examples