print.precision_profile

Displays a concise summary of precision profile results, including model
fit and functional sensitivity estimates.

Provides statistical methods for analytical method comparison and
validation studies. Implements Bland-Altman analysis for assessing agreement
between measurement methods (Bland & Altman (1986) <doi:10.1016/S0140-6736(86)90837-8>),
Passing-Bablok regression for non-parametric method comparison
(Passing & Bablok (1983) <doi:10.1515/cclm.1983.21.11.709>), and Deming
regression accounting for measurement error in both variables
(Linnet (1993) <doi:10.1093/clinchem/39.3.424>). Also includes tools for
setting quality goals based on biological variation (Fraser & Petersen (1993)
<doi:10.1093/clinchem/39.7.1447>) and calculating Six Sigma metrics,
precision experiments with variance component analysis, precision
profiles for functional sensitivity estimation (Kroll & Emancipator (1993)
<https://pubmed.ncbi.nlm.nih.gov/8448849/>). Commonly used in clinical
laboratory method validation. Provides publication-ready plots and
comprehensive statistical summaries.

Marcello Grassi

valytics

Statistical Methods for Analytical Method Comparison and
Validation

print.precision_profile function

<dl><dt>x</dt>
<dd>An object of class <code>precision_profile</code>.</dd>
<dt>digits</dt>
<dd>Number of significant digits to display (default: 3).</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Print method for precision_profile objects — print.precision_profile

<dl>

<dt>x</dt>
<dd>An object of class <code>precision_profile</code>.</dd>


<dt>digits</dt>
<dd>Number of significant digits to display (default: 3).</dd>


<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd>

</dl>

print.precision_profile: Print method for precision_profile objects

Description

Usage

Value

Arguments

Examples