mplot

Visualize marginals and Gaussian copula correlations of simulated data

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

mplot function

<dl><dt>obj</dt>
<dd>A MixtureModel object.</dd>
<dt>split</dt>
<dd>A logical, whether to display numbers on half of the covariance
matrix.</dd></dl>

Arguments

Visualize marginals and Gaussian copula correlations of simulated data — mplot

<dl>

<dt>obj</dt>
<dd>A MixtureModel object.</dd>


<dt>split</dt>
<dd>A logical, whether to display numbers on half of the covariance
matrix.</dd>

</dl>

mplot: Visualize marginals and Gaussian copula correlations of simulated data

Description

Usage

Value

Arguments