PLSModel

Function to perform partial least squares regression.

Meta-package for statistical and machine learning with a unified
interface for model fitting, prediction, performance assessment, and
presentation of results.  Approaches for model fitting and prediction of
numerical, categorical, or censored time-to-event outcomes include
traditional regression models, regularization methods, tree-based methods,
support vector machines, neural networks, ensembles, data preprocessing,
filtering, and model tuning and selection.  Performance metrics are provided
for model assessment and can be estimated with independent test sets, split
sampling, cross-validation, or bootstrap resampling.  Resample estimation
can be executed in parallel for faster processing and nested in cases of
model tuning and selection.  Modeling results can be summarized with
descriptive statistics; calibration curves; variable importance; partial
dependence plots; confusion matrices; and ROC, lift, and other performance
curves.

Brian Smith

MachineShop

Machine Learning Models and Tools

PLSModel function

<dl><dt>ncomp</dt>
<dd>number of components to include in the model.</dd>
<dt>scale</dt>
<dd>logical indicating whether to scale the predictors by the
sample standard deviation.</dd></dl>

Arguments

Partial Least Squares Model — PLSModel

<dl>

<dt>ncomp</dt>
<dd>number of components to include in the model.</dd>


<dt>scale</dt>
<dd>logical indicating whether to scale the predictors by the
sample standard deviation.</dd>

</dl>

PLSModel: Partial Least Squares Model

Description

Usage

Value

Arguments

Details

See Also

Examples