data_f_mi

<p>A simulated data set to experiment with nonparboot() with test = "F" and
missing outcomes</p>

datasets

Addressing crucial research questions often necessitates a small
sample size due to factors such as distinctive target populations, rarity
of the event under study, time and cost constraints, ethical concerns, or
group-level unit of analysis. Many readily available analytic methods,
however, do not accommodate small sample sizes, and the choice of the best
method can be unclear. The 'npboottprm' package enables the execution of
nonparametric bootstrap tests with pooled resampling to help fill this gap.
Grounded in the statistical methods for small sample size studies detailed
in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, the
package facilitates a range of statistical tests, encompassing independent
t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests.
The nonparboot() function undertakes essential computations, yielding
detailed outputs which include test statistics, effect sizes, confidence
intervals, and bootstrap distributions. Further, 'npboottprm' incorporates
an interactive 'shiny' web application, nonparboot_app(), offering intuitive,
user-friendly data exploration.

Mackson Ncube

npboottprm

Nonparametric Bootstrap Test with Pooled Resampling

mightymetrika, LLC 

data_f_mi function


<div class="section" id="data-f-mi">
<h3><code>data_f_mi</code></h3><p></p><p>
</p><p>A data frame with 15 rows and 2 columns:</p><dl>
<dt>x</dt>
<dd><p>A numeric variable</p></dd><p></p><p><dt>grp</dt>
<dd><p>A factor variable with group labels</p></dd></p><p>
</p></dl><p></p><p></p></div>


Format

Simulated Data for F-test with Missing Outcomes — data_f_mi


<div class='section' id='data-f-mi'>
<h3><code>data_f_mi</code></h3>


<p>A data frame with 15 rows and 2 columns:</p><dl>
<dt>x</dt>
<dd><p>A numeric variable</p></dd>

<dt>grp</dt>
<dd><p>A factor variable with group labels</p></dd>


</dl>

</div>


data_f_mi: Simulated Data for F-test with Missing Outcomes

Description

Usage

Arguments

Format

`data_f_mi`