CorrStructure

autocor

blockcor

Functions to create autocorrelation matrix (p by p) with parameter rho and block correlation matrix (p by p) using group index (of length p) and (possibly) different parameter rho for each group.

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

CorrStructure function

<dl><dt>p</dt>
<dd>Specified matrix dimension.</dd>
<dt>rho</dt>
<dd>Correlation value(s), must be between -0.99 and 0.99. Should be a scalar for <code>autocor</code>, and either a scalar or a vector of the same length as the maximal <code>blockind</code> K for <code>blockcor</code>.</dd>
<dt>blockind</dt>
<dd>Block index 1,..., K for a positive integer K specifying which variable belongs to which block, the matrix dimension is equal to <code>length(blockind)</code>.</dd></dl>

Arguments

Construct a correlation matrix — CorrStructure

<dl>

<dt>p</dt>
<dd>Specified matrix dimension.</dd>


<dt>rho</dt>
<dd>Correlation value(s), must be between -0.99 and 0.99. Should be a scalar for <code>autocor</code>, and either a scalar or a vector of the same length as the maximal <code>blockind</code> K for <code>blockcor</code>.</dd>


<dt>blockind</dt>
<dd>Block index 1,..., K for a positive integer K specifying which variable belongs to which block, the matrix dimension is equal to <code>length(blockind)</code>.</dd>

</dl>

CorrStructure: Construct a correlation matrix

Description

Usage

Arguments

Examples