get_funnel_plot

High-level wrapper that accepts the original input data and the result returned by
`maive()` / `waive()`, prepares the necessary vectors/metadata, and draws the funnel plot
on the currently active graphics device.

Meta-analysis traditionally assigns more weight to studies with lower standard errors,
assuming higher precision. However, in observational research, precision must be
estimated and is vulnerable to manipulation, such as p-hacking, to achieve statistical
significance. This can lead to spurious precision, invalidating inverse-variance
weighting and bias-correction methods like funnel plots. Common methods for addressing
publication bias, including selection models, often fail or exacerbate the problem.
This package introduces an instrumental variable approach to limit bias caused by
spurious precision in meta-analysis. Methods are described in 'Irsova et al.' (2025)
<doi:10.1038/s41467-025-63261-0>.

Petr Cala

MAIVE

Meta Analysis Instrumental Variable Estimator

Zuzana Irsova

Pedro R. D. Bom

Tomas Havranek

Heiko Rachinger

get_funnel_plot function

<dl><dt>dat</dt>
<dd>Data frame containing at least numeric columns `bs` (effect sizes)
and `sebs` (standard errors).</dd>
<dt>result</dt>
<dd>Result list returned by `maive()` or `waive()`.</dd>
<dt>instrument</dt>
<dd>Optional indicator (0/1). If `NULL`, inferred from `result`.</dd>
<dt>model_type</dt>
<dd>Label used for plot text/legend (e.g., `"MAIVE"` or `"WAIVE"`).</dd></dl>

Arguments

Draw a funnel plot (base graphics) — get_funnel_plot

<dl>

<dt>dat</dt>
<dd>Data frame containing at least numeric columns `bs` (effect sizes)
and `sebs` (standard errors).</dd>


<dt>result</dt>
<dd>Result list returned by `maive()` or `waive()`.</dd>


<dt>instrument</dt>
<dd>Optional indicator (0/1). If `NULL`, inferred from `result`.</dd>


<dt>model_type</dt>
<dd>Label used for plot text/legend (e.g., `"MAIVE"` or `"WAIVE"`).</dd>

</dl>

get_funnel_plot: Draw a funnel plot (base graphics)

Description

Usage

Value

Arguments

Details