partial

Partial correlations between elements in x and elements in y

A flexible framework for power analysis using Monte
Carlo simulation for settings in which considerations of the correlations
between predictors are important. Users can set up a data generative model
that preserves dependence structures among predictors given existing data
(continuous, binary, or ordinal). Users can also generate power curves to
assess the trade-offs between sample size, effect size, and power of a design.
This package includes several statistical models common in environmental
mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.

Phuc H. Nguyen

mpower

Power Analysis via Monte Carlo Simulation for Correlated Data

partial function

<dl><dt>r</dt>
<dd>A correlation matrix</dd>
<dt>x</dt>
<dd>A vector of indices</dd>
<dt>y</dt>
<dd>A vector of indices</dd></dl>

Arguments

Partial correlations between elements in x and elements in y — partial

<dl>

<dt>r</dt>
<dd>A correlation matrix</dd>


<dt>x</dt>
<dd>A vector of indices</dd>


<dt>y</dt>
<dd>A vector of indices</dd>

</dl>

partial: Partial correlations between elements in x and elements in y

Description

Usage

Value

Arguments