OutcomeModel

This function creates a generative model of the outcome given a matrix of
predictors.

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

OutcomeModel function

<dl><dt>f</dt>
<dd>A string that describes the relationships between the predictors and
outcome or a function that takes an input matrix and returns a vector of
outcome: \(E(y|x) = g(f(x))\) where g is a link function that depends on
the family argument.</dd>
<dt>family</dt>
<dd>A string, 'gaussian', 'binomial', or 'poisson' for continuous,
binary, or count outcomes.</dd>
<dt>sigma</dt>
<dd>A number, Gaussian noise standard deviation if applicable.</dd>
<dt>f_args</dt>
<dd>A named list of additional arguments to f</dd></dl>

Arguments

Outcome generator — OutcomeModel

<dl>

<dt>f</dt>
<dd>A string that describes the relationships between the predictors and
outcome or a function that takes an input matrix and returns a vector of
outcome: \(E(y|x) = g(f(x))\) where g is a link function that depends on
the family argument.</dd>


<dt>family</dt>
<dd>A string, 'gaussian', 'binomial', or 'poisson' for continuous,
binary, or count outcomes.</dd>


<dt>sigma</dt>
<dd>A number, Gaussian noise standard deviation if applicable.</dd>


<dt>f_args</dt>
<dd>A named list of additional arguments to f</dd>

</dl>

OutcomeModel: Outcome generator

Description

Usage

Value

Arguments

Examples