bma_wrapper

Fits a linear model with Bayesian model selection with significance criteria:
PIP and posterior probability of nonzero coefficients being on one side of
zero.

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

bma_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 `BMA::bic.glm()`.</dd></dl>

Arguments

Raftery A, Hoeting J, Volinsky C, Painter I, Yeung KY (2021).BMA: Bayesian
 model averaging. R package version 3.18.15.

Reference

Fits a linear model with Bayesian model selection with significance criteria:
PIP and posterior probability of nonzero coefficients being on one side of
zero. — bma_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 `BMA::bic.glm()`.</dd>

</dl>

Raftery A, Hoeting J, Volinsky C, Painter I, Yeung KY (2021).BMA: Bayesian
 model averaging. R package version 3.18.15.

Fits a linear model with Bayesian model selection with significance criteria:
PIP and posterior probability of nonzero coefficients being on one side of
zero.

bma_wrapper: Fits a linear model with Bayesian model selection with significance criteria: PIP and posterior probability of nonzero coefficients being on one side of zero.

Description

Usage

Value

Arguments

Reference