simdata

This is a function for generating multiblock data based on the multivariable normal distribusion

Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.

Atsushi Kawaguchi

msma

Multiblock Sparse Multivariable Analysis

simdata function

<dl><dt>n</dt>
<dd>a numeric scalar, sample size.</dd>
<dt>rho</dt>
<dd>a numeric scalar, correlation coefficient.</dd>
<dt>Yps</dt>
<dd>a numeric vector, numbers of columns for Y. The length of vector corresponds to the number of blocks.</dd>
<dt>Xps</dt>
<dd>a numeric vector, numbers of columns for X. The length of vector corresponds to the number of blocks.</dd>
<dt>seed</dt>
<dd>a seed number for generating random numbers.</dd></dl>

Arguments

Simulate Data sets — simdata

<dl>

<dt>n</dt>
<dd>a numeric scalar, sample size.</dd>


<dt>rho</dt>
<dd>a numeric scalar, correlation coefficient.</dd>


<dt>Yps</dt>
<dd>a numeric vector, numbers of columns for Y. The length of vector corresponds to the number of blocks.</dd>


<dt>Xps</dt>
<dd>a numeric vector, numbers of columns for X. The length of vector corresponds to the number of blocks.</dd>


<dt>seed</dt>
<dd>a seed number for generating random numbers.</dd>

</dl>

simdata: Simulate Data sets

Description

Usage

Value

Arguments

Details