scale_sigma

Rescale the noise variance of a Gaussian OutcomeModel to meet a given SNR

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

scale_sigma function

<dl><dt>snr</dt>
<dd>A SNR</dd>
<dt>ymod</dt>
<dd>A OutcomeModel object to modify</dd>
<dt>xmod</dt>
<dd>A MixtureModel object</dd>
<dt>m</dt>
<dd>Number of MC samples to estimate the SNR of a proposed noise variance</dd></dl>

Arguments

Rescale the noise variance of a Gaussian OutcomeModel to meet a given SNR — scale_sigma

<dl>

<dt>snr</dt>
<dd>A SNR</dd>


<dt>ymod</dt>
<dd>A OutcomeModel object to modify</dd>


<dt>xmod</dt>
<dd>A MixtureModel object</dd>


<dt>m</dt>
<dd>Number of MC samples to estimate the SNR of a proposed noise variance</dd>

</dl>

scale_sigma: Rescale the noise variance of a Gaussian OutcomeModel to meet a given SNR

Description

Usage

Value

Arguments