generate_descriptives

This internal helper function generates descriptive statistics for each group
within the original study data. It calculates the sample size, mean, standard
deviation, median, median absolute deviation, minimum, maximum, skewness, and
kurtosis for the values associated with each group.

internal

Researchers often have expectations about the relations between means
of different groups or standardized regression coefficients; using informative
hypothesis testing to incorporate these expectations into the analysis through
order constraints increases statistical power
Vanbrabant and Rosseel (2020) <doi:10.4324/9780429273872-14>. Another valuable
tool, the Bayes factor, can evaluate evidence for multiple hypotheses without
concerns about multiple testing, and can be used in Bayesian updating
Hoijtink, Mulder, van Lissa & Gu (2019) <doi:10.1037/met0000201>. The 'bain'
R package enables informative hypothesis testing using the Bayes factor. The
'mmibain' package provides 'shiny' web applications based on 'bain'. The
RepliCrisis() function launches a 'shiny' card game to simulate the evaluation
of replication studies while the mmibain() function launches a 'shiny'
application to fit Bayesian informative hypotheses evaluation models from
'bain'.

Mackson Ncube

mmibain

Bayesian Informative Hypotheses Evaluation Web Applications

mightymetrika, LLC 

generate_descriptives function

<dl><dt>df</dt>
<dd>A data frame containing the study data.</dd>
<dt>group_var</dt>
<dd>The name of the column in <code>df</code> that contains the group labels.</dd></dl>

Arguments

Generate Descriptive Statistics for Study Groups — generate_descriptives

<dl>

<dt>df</dt>
<dd>A data frame containing the study data.</dd>


<dt>group_var</dt>
<dd>The name of the column in <code>df</code> that contains the group labels.</dd>

</dl>

generate_descriptives: Generate Descriptive Statistics for Study Groups

Description

Usage

Arguments

Details