.sample_design_prior

Sample a true effect from a design-prior specification

internal

Provides tools for Bayesian power analysis and assurance calculations using the statistical frameworks of 'brms' and 'INLA'. Includes simulation-based approaches, support for multiple decision rules (direction, threshold, ROPE), sequential designs, and visualisation helpers. Methods are based on Kruschke (2014, ISBN:9780124058880) "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan", O'Hagan & Stevens (2001) <doi:10.1177/0272989X0102100307> "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness", Kruschke (2018) <doi:10.1177/2515245918771304> "Rejecting or Accepting Parameter Values in Bayesian Estimation", Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x> "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", and Bürkner (2017) <doi:10.18637/jss.v080.i01> "brms: An R Package for Bayesian Multilevel Models using Stan".

Tony Myers

powerbrmsINLA

Bayesian Power Analysis Using 'brms' and 'INLA'

.sample_design_prior function

<dl><dt>spec</dt>
<dd>List with a <code>dist</code> element: <code>"normal"</code> (mean, sd),
<code>"uniform"</code> (min, max) or <code>"beta"</code> (shape1, shape2).</dd>
<dt>n</dt>
<dd>Number of draws.</dd></dl>

Arguments

Sample a true effect from a design-prior specification — .sample_design_prior

<dl>

<dt>spec</dt>
<dd>List with a <code>dist</code> element: <code>"normal"</code> (mean, sd),
<code>"uniform"</code> (min, max) or <code>"beta"</code> (shape1, shape2).</dd>


<dt>n</dt>
<dd>Number of draws.</dd>

</dl>

.sample_design_prior: Sample a true effect from a design-prior specification

Description

Usage

Value

Arguments