summary

Tabular summaries of power simulation

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

summary function

<dl><dt>sim</dt>
<dd>A Sim or a SimCurve object, output from `sim_power()` or
`sim_curve()`.</dd>
<dt>crit</dt>
<dd>A string specifying the significance criteria.</dd>
<dt>thres</dt>
<dd>A number or vector of numbers specifying the thresholds of
"significance".</dd>
<dt>digits</dt>
<dd>An integer for the number of decimal points to display.</dd>
<dt>how</dt>
<dd>A string, whether to compare the criterion 'greater' or 'lesser'
than the threshold.</dd></dl>

Arguments

Tabular summaries of power simulation — summary

<dl>

<dt>sim</dt>
<dd>A Sim or a SimCurve object, output from `sim_power()` or
`sim_curve()`.</dd>


<dt>crit</dt>
<dd>A string specifying the significance criteria.</dd>


<dt>thres</dt>
<dd>A number or vector of numbers specifying the thresholds of
"significance".</dd>


<dt>digits</dt>
<dd>An integer for the number of decimal points to display.</dd>


<dt>how</dt>
<dd>A string, whether to compare the criterion 'greater' or 'lesser'
than the threshold.</dd>

</dl>

summary: Tabular summaries of power simulation

Description

Usage

Value

Arguments