plot_mu

Based on the data before and after the intervention and the regression models from the function <code>meechua_reg</code>, this function plots the t-statistics and p-values for a given range of \(\mu\) to assess whether the intervention has a significant impact on the measurements, accounting for regression to the mean.

In repeated measures studies with extreme large or small values it is common
that the subjects measurements on average are closer to the mean of the basic population.
Interpreting possible changes in the mean in such situations can lead to biased results
since the values were not randomly selected, they come from truncated sampling.
This method allows to estimate the range of means where treatment effects are likely to occur
when regression toward the mean is present.
Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.<doi:10.1186/1471-2288-8-52>.
Acknowledgments: We would like to acknowledge "Lena Roth" and "Nico Steckhan" for the package's initial updates (Q3 2024) and continued supervision and guidance. Both have contributed to discussing and integrating these methods into the package, ensuring they are up-to-date and contextually relevant.

Daniela Recchia

regtomean

Regression Toward the Mean

Thomas Ostermann

Julian Stein

plot_mu function

<dl><dt>x</dt>
<dd>A data frame containing the results from <code>meechua_reg</code>. Specifically, this should be the <code>mod_coef</code> data frame obtained from <code>meechua_reg</code>.</dd>
<dt>n</dt>
<dd>The original sample size (number of observations) of the data.</dd>
<dt>se_after</dt>
<dd>The estimated standard error from <code>meechua_reg</code>. This should be the <code>se_after</code> vector obtained from <code>meechua_reg</code>.</dd>
<dt>lower</dt>
<dd>A boolean value specifying the direction of the one-sided tests. For <code>lower = FALSE</code> (the default), it tests whether the intervention is increasing the measurements. For <code>lower = TRUE</code>, it tests whether the second measurements are lower than expected.</dd>
<dt>alpha</dt>
<dd>Specifies the significance threshold for the p-values of the corresponding one-sided tests. The default is <code>alpha = 0.05</code>.</dd></dl>

Arguments

Author

Plot t-Statistics and p-Values for Intervention Impact — plot_mu

<dl>

<dt>x</dt>
<dd>A data frame containing the results from <code>meechua_reg</code>. Specifically, this should be the <code>mod_coef</code> data frame obtained from <code>meechua_reg</code>.</dd>


<dt>n</dt>
<dd>The original sample size (number of observations) of the data.</dd>


<dt>se_after</dt>
<dd>The estimated standard error from <code>meechua_reg</code>. This should be the <code>se_after</code> vector obtained from <code>meechua_reg</code>.</dd>


<dt>lower</dt>
<dd>A boolean value specifying the direction of the one-sided tests. For <code>lower = FALSE</code> (the default), it tests whether the intervention is increasing the measurements. For <code>lower = TRUE</code>, it tests whether the second measurements are lower than expected.</dd>


<dt>alpha</dt>
<dd>Specifies the significance threshold for the p-values of the corresponding one-sided tests. The default is <code>alpha = 0.05</code>.</dd>

</dl>

plot_mu: Plot t-Statistics and p-Values for Intervention Impact

Description

Usage

Value

Arguments

Author

References

Examples