fin_wrapper

Fits a Bayesian factor model with interactions

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

fin_wrapper function

<dl><dt>y</dt>
<dd>A vector of outcome</dd>
<dt>x</dt>
<dd>A matrix of predictors</dd>
<dt>args</dt>
<dd>A list of arguments see R function
`infinitefactor::interactionDL()` in 'infinitefactor' package.</dd></dl>

Arguments

Ferrari F, Dunson DB (2020). “Bayesian factor analysis for inference on
 interactions.”Journal of the American Statistical Association, 0(0), 1–12.

Reference

Fits a Bayesian factor model with interactions — fin_wrapper

<dl>

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


<dt>x</dt>
<dd>A matrix of predictors</dd>


<dt>args</dt>
<dd>A list of arguments see R function
`infinitefactor::interactionDL()` in 'infinitefactor' package.</dd>

</dl>

Ferrari F, Dunson DB (2020). “Bayesian factor analysis for inference on
 interactions.”Journal of the American Statistical Association, 0(0), 1–12.

fin_wrapper: Fits a Bayesian factor model with interactions

Description

Usage

Value

Arguments

Reference