lplsData

Three data blocks are simulated to express covariance in an exo-L-PLS direction (see <code>lpls</code>.
Dimensionality and number of underlying components can be controlled.

Functions and datasets to support Smilde, Næs and Liland (2021, ISBN: 978-1-119-60096-1)
"Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences".
This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting
functions, several real data sets and six vignettes covering a range different applications.

Kristian Hovde Liland

multiblock

Multiblock Data Fusion in Statistics and Machine Learning

Solve Sæbø

Stefan Schrunner

lplsData function

<dl><dt>I</dt>
<dd><code>numeric</code> number of rows of X1 and X2</dd>
<dt>N</dt>
<dd><code>numeric</code> number of columns in X1 and X3</dd>
<dt>J</dt>
<dd><code>numeric</code> number of columns in X2</dd>
<dt>K</dt>
<dd><code>numeric</code> number of rows in X3</dd>
<dt>ncomp</dt>
<dd><code>numeric</code> number of latent components</dd></dl>

Arguments

Solve Sæbø (adapted by Kristian Hovde Liland)

Author

L-PLS data simulation for exo-type analysis — lplsData

<dl>

<dt>I</dt>
<dd><code>numeric</code> number of rows of X1 and X2</dd>


<dt>N</dt>
<dd><code>numeric</code> number of columns in X1 and X3</dd>


<dt>J</dt>
<dd><code>numeric</code> number of columns in X2</dd>


<dt>K</dt>
<dd><code>numeric</code> number of rows in X3</dd>


<dt>ncomp</dt>
<dd><code>numeric</code> number of latent components</dd>

</dl>

lplsData: L-PLS data simulation for exo-type analysis

Description

Usage

Value

Arguments

Author

See Also

Examples