naiveBayes_dynamicPrior

internal

Performs parametric synthesis of sounds with harmonic and noise
components such as animal vocalizations or human voice. Also offers tools
for audio manipulation and acoustic analysis, including pitch tracking,
spectral analysis, audio segmentation, pitch and formant shifting, etc.
Includes four interactive web apps for synthesizing and annotating audio,
manually correcting pitch contours, and measuring formant frequencies.
Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.

Andrey Anikin

soundgen

Sound Synthesis and Acoustic Analysis

naiveBayes_dynamicPrior function

<dl><dt>d</dt>
<dd>dataframe containing the observations</dd>
<dt>nObs</dt>
<dd>the number of observations</dd>
<dt>mod_train</dt>
<dd>the output of naiveBayes_train()</dd>
<dt>wl</dt>
<dd>window length, points</dd>
<dt>class_names</dt>
<dd>names of outcome classes</dd>
<dt>nClasses</dt>
<dd>the number of outcome classes</dd>
<dt>like_names</dt>
<dd>the names of variables holding likelihoods</dd>
<dt>prior_names</dt>
<dd>the names of prior variables</dd>
<dt>predictors</dt>
<dd>the names of predictor variables</dd>
<dt>nPredictors</dt>
<dd>the number of predicto variables</dd></dl>

Arguments

Naive Bayes dynamic prior — naiveBayes_dynamicPrior

<dl>

<dt>d</dt>
<dd>dataframe containing the observations</dd>


<dt>nObs</dt>
<dd>the number of observations</dd>


<dt>mod_train</dt>
<dd>the output of naiveBayes_train()</dd>


<dt>wl</dt>
<dd>window length, points</dd>


<dt>class_names</dt>
<dd>names of outcome classes</dd>


<dt>nClasses</dt>
<dd>the number of outcome classes</dd>


<dt>like_names</dt>
<dd>the names of variables holding likelihoods</dd>


<dt>prior_names</dt>
<dd>the names of prior variables</dd>


<dt>predictors</dt>
<dd>the names of predictor variables</dd>


<dt>nPredictors</dt>
<dd>the number of predicto variables</dd>

</dl>

naiveBayes_dynamicPrior: Naive Bayes dynamic prior

Description

Usage

Arguments

Details