multi_predict._mixo_pls

multi_predict._mixo_spls

multi_predict._mixo_plsda

multi_predict._mixo_splsda

Model predictions across many sub-models

Bindings for additional regression models for use with the
'parsnip' package, including ordinary and spare partial least squares
models for regression and classification (Rohart et al (2017)
<doi:10.1371/journal.pcbi.1005752>).

Max Kuhn

plsmod

Model Wrappers for Projection Methods

RStudio 

multi_predict._mixo_pls function

<dl><dt>object</dt>
<dd>An object of class <code>model_fit</code></dd>
<dt>new_data</dt>
<dd>A rectangular data object, such as a data frame.</dd>
<dt>num_comp</dt>
<dd>An integer vector for the number of PLS terms to retain.</dd>
<dt>type</dt>
<dd>A single character value or <code>NULL</code>. Possible values
are "numeric", "class", or "prob". When <code>NULL</code>, <code><a href="/link/predict()?package=plsmod&version=1.0.0" data-mini-rdoc="plsmod::predict()">predict()</a></code> will choose an
appropriate value based on the model's mode.</dd>
<dt>...</dt>
<dd>Not currently used.</dd></dl>

Arguments

Model predictions across many sub-models — multi_predict._mixo_pls

<dl>

<dt>object</dt>
<dd>An object of class <code>model_fit</code></dd>


<dt>new_data</dt>
<dd>A rectangular data object, such as a data frame.</dd>


<dt>num_comp</dt>
<dd>An integer vector for the number of PLS terms to retain.</dd>


<dt>type</dt>
<dd>A single character value or <code>NULL</code>. Possible values
are "numeric", "class", or "prob". When <code>NULL</code>, <code><a href='https://rdrr.io/r/stats/predict.html'>predict()</a></code> will choose an
appropriate value based on the model's mode.</dd>


<dt>...</dt>
<dd>Not currently used.</dd>

</dl>

multi_predict._mixo_pls: Model predictions across many sub-models

Description

Usage

Arguments