tool

normalize.set

scaledlasso

internal

A statistical tool to inference the multi-level partial correlations based on multi-subject time series data, especially for brain functional connectivity. It combines both individual and population level inference by using the methods of Qiu and Zhou. (2021)<DOI: 10.1080/01621459.2021.1917417> and Genovese and Wasserman. (2006)<DOI: 10.1198/016214506000000339>. It realizes two reliable estimation methods of partial correlation coefficients, using scaled lasso and lasso. It can be used to estimate individual- or population-level partial correlations, identify nonzero ones, and find out unequal partial correlation coefficients between two populations.

Yunhaonan Yang

BrainCon

Inference the Partial Correlations Based on Time Series Data

Peng Wu

Xin Gai

Yumou Qiu

Xiaohua Zhou

tool function

<dl><dt>u</dt>
<dd>numeric value.</dd>
<dt>set</dt>
<dd>two-column numeric matrix.</dd>
<dt>p</dt>
<dd>the number of variables.</dd>
<dt>X</dt>
<dd>the input matrix of scaled lasso.</dd>
<dt>y</dt>
<dd>response variable of scaled lasso.</dd>
<dt>lam0</dt>
<dd>numeric value, the penalty parameter of scaled lasso.</dd></dl>

Arguments

tool functions — tool

<dl>

<dt>u</dt>
<dd>numeric value.</dd>


<dt>set</dt>
<dd>two-column numeric matrix.</dd>


<dt>p</dt>
<dd>the number of variables.</dd>


<dt>X</dt>
<dd>the input matrix of scaled lasso.</dd>


<dt>y</dt>
<dd>response variable of scaled lasso.</dd>


<dt>lam0</dt>
<dd>numeric value, the penalty parameter of scaled lasso.</dd>

</dl>

tool functions

tool: tool functions

Description

Usage

Value

Arguments