summary.textmodel_lr

summary method for textmodel_lr objects

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 

summary.textmodel_lr function

<dl><dt>object</dt>
<dd>output from <code>textmodel_lr()</code></dd>
<dt>n</dt>
<dd>how many coefficients to print before truncating</dd>
<dt>...</dt>
<dd>additional arguments not used</dd></dl>

Arguments

summary method for textmodel_lr objects — summary.textmodel_lr

<dl>

<dt>object</dt>
<dd>output from <code>textmodel_lr()</code></dd>


<dt>n</dt>
<dd>how many coefficients to print before truncating</dd>


<dt>...</dt>
<dd>additional arguments not used</dd>

</dl>

summary method for textmodel_lr objects

summary.textmodel_lr: summary method for textmodel_lr objects

Description

Usage

Arguments