plot_simple_slope

Plots the simple slope min and max estimates from power_interaction().

Power analysis for regression models which test the interaction of
two independent variables on a single dependent variable. Includes options
for continuous, binary, or ordinal variables, as well as correlated
interacting variables. Also includes options to specify variable reliability.
Power analyses can be done either analytically or via simulation. Includes
tools for simulating single data sets and visualizing power analysis results.
The primary functions are power_interaction_r2() and power_interaction().
Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR,
Olino TM (2022). "Tutorial: Power analyses for interaction effects in
cross-sectional regressions." <doi:10.31234/osf.io/5ptd7>.

David Baranger

InteractionPoweR

Power Analyses for Interaction Effects in Cross-Sectional
Regressions

Brandon Goldstein

Megan Finsaas

Thomas Olino

Colin Vize

Don Lynam

plot_simple_slope function

<dl><dt>power_data</dt>
<dd>Data frame of results from power_interaction(). Can accept the raw results if up to 2 parameters were varied during simulation. Any more and data should be filtered first.</dd>
<dt>x</dt>
<dd>Optional, the x-axis of the plot. Default is the first variable after 'pwr'.</dd>
<dt>facets</dt>
<dd>Optional, grouping variable for plot facets. Default is the second variable after 'pwr' if present.</dd></dl>

Arguments

Simple slope plot — plot_simple_slope

<dl>

<dt>power_data</dt>
<dd>Data frame of results from power_interaction(). Can accept the raw results if up to 2 parameters were varied during simulation. Any more and data should be filtered first.</dd>


<dt>x</dt>
<dd>Optional, the x-axis of the plot. Default is the first variable after 'pwr'.</dd>


<dt>facets</dt>
<dd>Optional, grouping variable for plot facets. Default is the second variable after 'pwr' if present.</dd>

</dl>

plot_simple_slope: Simple slope plot

Description

Usage

Value

Arguments

Examples