generate_coefficient

Using the Fisher Z Transformation, generate new
validity coefficients based on proposed upper and lower boundaries.
This new validity coefficient is based on transformed upper and lower
boundaries as well as a fitted normal distribution to the se new
proposed upper and lower boundaries.

A methodology to perform multivariate measurement error adjustment using external validation data. Allows users to remove the attenuating effect of measurement error by incorporating a distribution of external validation data, and allows for plotting of all resultant adjustments. Sensitivity analyses can also be run through this package to test how different ranges of validity coefficients can impact the effect of the measurement error adjustment. The methods implemented in this package are based on the work by Muoka, A., Agogo, G., Ngesa, O., Mwambi, H. (2020): <doi:10.12688/f1000research.27892.1>.

Alexander Lee

rACMEMEEV

Multi-Variate Measurement Error Adjustment

generate_coefficient function

<dl><dt>n</dt>
<dd>the samples for the normal distribution</dd>
<dt>lower_bound</dt>
<dd>numeric the lower boundary of the validity coefficient</dd>
<dt>upper_bound</dt>
<dd>numeric the upper boudnary of the validity coefficient</dd>
<dt>interval</dt>
<dd>numeric the confidence interval to use (default 0.95)</dd>
<dt>seed</dt>
<dd>numeric the random seed to use</dd></dl>

Arguments

Generate updated validity coefficient using Fisher Z Transformation — generate_coefficient

<dl>

<dt>n</dt>
<dd>the samples for the normal distribution</dd>


<dt>lower_bound</dt>
<dd>numeric the lower boundary of the validity coefficient</dd>


<dt>upper_bound</dt>
<dd>numeric the upper boudnary of the validity coefficient</dd>


<dt>interval</dt>
<dd>numeric the confidence interval to use (default 0.95)</dd>


<dt>seed</dt>
<dd>numeric the random seed to use</dd>

</dl>

generate_coefficient: Generate updated validity coefficient using Fisher Z Transformation

Description

Usage

Value

Arguments

Examples