standardCCA

This function is modified from original CCA function for two reasons: to deal with only positive eigenvalues larger than the tolerance when calculating the inverse of the matrices and to compute Singular Value Decomposition using <code>irlba</code> algorithm. Inputs should be correlation or covariance matrices of each data set and between datasets. This function returns only the first pair of canonical covariates.

Semi-parametric approach for sparse canonical correlation analysis
which can handle mixed data types: continuous, binary and truncated continuous.
Bridge functions are provided to connect Kendall's tau to latent correlation
under the Gaussian copula model. The methods are described in
Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and
Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.

Irina Gaynanova

mixedCCA

Sparse Canonical Correlation Analysis for High-Dimensional Mixed
Data

Grace Yoon

Mingze Huang

standardCCA function

<dl><dt>S1</dt>
<dd>correlation/covariance matrix of dataset <code>X1</code>.</dd>
<dt>S2</dt>
<dd>correlation/covariance matrix of dataset <code>X2</code>.</dd>
<dt>S12</dt>
<dd>correlation/covariance matrix between dataset <code>X1</code> and dataset <code>X2</code>.</dd>
<dt>tol</dt>
<dd>tolerance for eigenvalues. <code>standardCCA</code> function only deals with positive eigenvalues larger than the tolerance.</dd></dl>

Arguments

Internal standard CCA function. — standardCCA

<dl>

<dt>S1</dt>
<dd>correlation/covariance matrix of dataset <code>X1</code>.</dd>


<dt>S2</dt>
<dd>correlation/covariance matrix of dataset <code>X2</code>.</dd>


<dt>S12</dt>
<dd>correlation/covariance matrix between dataset <code>X1</code> and dataset <code>X2</code>.</dd>


<dt>tol</dt>
<dd>tolerance for eigenvalues. <code>standardCCA</code> function only deals with positive eigenvalues larger than the tolerance.</dd>

</dl>

standardCCA: Internal standard CCA function.

Description

Usage

Value

Arguments