DxPlotPost

Creates spaghetti-style diagnostic plots with individual ID labels.

This shows how NONMEM(R) software works. NONMEM's classical
estimation methods like 'First Order(FO) approximation', 'First Order
Conditional Estimation(FOCE)', and 'Laplacian approximation' are explained.
Additionally, provides functions for post-run processing of NONMEM output
files, generating comprehensive PDF diagnostic reports including objective
function value analysis, parameter estimates, prediction diagnostics,
residual diagnostics, empirical Bayes estimate (EBE) analysis, input data
summary, and individual pharmacokinetic parameter distributions.

Kyun-Seop Bae

Understanding Nonlinear Mixed Effects Modeling for Population
Pharmacokinetics

DxPlotPost function

<dl><dt>x</dt>
<dd>numeric vector, x-axis values</dd>
<dt>y</dt>
<dd>numeric vector, y-axis values</dd>
<dt>mat</dt>
<dd>data.frame with an ID column</dd>
<dt>xlbl</dt>
<dd>character, x-axis label</dd>
<dt>ylbl</dt>
<dd>character, y-axis label</dd>
<dt>smooth</dt>
<dd>character, "T" for lowess smoothing, "F" for identity line</dd>
<dt>xlm</dt>
<dd>numeric vector of length 2, x-axis limits</dd>
<dt>ylm</dt>
<dd>numeric vector of length 2, y-axis limits</dd>
<dt>Log</dt>
<dd>character, log transformation for axes (e.g., "y")</dd></dl>

Arguments

Diagnostic Plot for Post-Processing — DxPlotPost

<dl>

<dt>x</dt>
<dd>numeric vector, x-axis values</dd>


<dt>y</dt>
<dd>numeric vector, y-axis values</dd>


<dt>mat</dt>
<dd>data.frame with an ID column</dd>


<dt>xlbl</dt>
<dd>character, x-axis label</dd>


<dt>ylbl</dt>
<dd>character, y-axis label</dd>


<dt>smooth</dt>
<dd>character, "T" for lowess smoothing, "F" for identity line</dd>


<dt>xlm</dt>
<dd>numeric vector of length 2, x-axis limits</dd>


<dt>ylm</dt>
<dd>numeric vector of length 2, y-axis limits</dd>


<dt>Log</dt>
<dd>character, log transformation for axes (e.g., "y")</dd>

</dl>

DxPlotPost: Diagnostic Plot for Post-Processing

Description

Usage

Arguments