BinaryPLSFit

Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in 'Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.

Jose Vicente-Villardon

MultBiplotR

Multivariate Analysis Using Biplots in R

BinaryPLSFit function

<dl> <dt>Y</dt>
<dd>The response</dd> <dt>X</dt>
<dd>The matrix of independent variables</dd> <dt>S</dt>
<dd>The Dimension of the solution</dd> <dt>tolerance</dt>
<dd>Tolerance for convergence of the algorithm</dd> <dt>maxiter</dt>
<dd>Maximum Number of iterations</dd> <dt>show</dt>
<dd>Show the steps of the algorithm</dd> <dt>penalization</dt>
<dd>Penalization for the Ridge Logistic Regression</dd> <dt>OptimMethod</dt>
<dd>Optimization methods from optimr</dd> <dt>seed</dt>
<dd>Seed. By default is 0.</dd></dl>

Arguments

Author

Binary PLS Regression. — BinaryPLSFit

<dl>

 <dt>Y</dt>
<dd>The response</dd>

 <dt>X</dt>
<dd>The matrix of independent variables</dd>

 <dt>S</dt>
<dd>The Dimension of the solution</dd>

 <dt>tolerance</dt>
<dd>Tolerance for convergence of the algorithm</dd>

 <dt>maxiter</dt>
<dd>Maximum Number of iterations</dd>

 <dt>show</dt>
<dd>Show the steps of the algorithm</dd>

 <dt>penalization</dt>
<dd>Penalization for the Ridge Logistic Regression</dd>

 <dt>OptimMethod</dt>
<dd>Optimization methods from optimr</dd>

 <dt>seed</dt>
<dd>Seed. By default is 0.</dd>

</dl>

BinaryPLSFit: Binary PLS Regression.

Description

Usage

Value

Arguments

Author

Details

References

Examples