plot_pmx.residual

This function plots residual for each observed value by finding the difference between observed and predicted points. It also fits a distribution to the residual value.

At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling
activities in order to reduce the overall effort required for generating such plots.
For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox,
called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient
for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP
<doi:10.1002/psp4.12161>. While not required, you can get/install the 'R' 'lixoftConnectors'
package in the 'Monolix' installation, as described at the following url
<https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/>.
When 'lixoftConnectors' is available, 'R' can use 'Monolix' directly to create the required
Chart Data instead of exporting it from the 'Monolix' gui.

Matthew Fidler

ggPMX

'ggplot2' Based Tool to Facilitate Diagnostic Plots for NLME
Models

plot_pmx.residual function

<dl><dt>x</dt>
<dd>residual object</dd>
<dt>dx</dt>
<dd>data set</dd>
<dt>...</dt>
<dd>not used for the moment</dd></dl>

Arguments

This function plots residual for each observed value by finding the difference between observed and predicted points. It also fits a distribution to the residual value. — plot_pmx.residual

<dl>

<dt>x</dt>
<dd>residual object</dd>


<dt>dx</dt>
<dd>data set</dd>


<dt>...</dt>
<dd>not used for the moment</dd>

</dl>

This function plots residual for each observed value by finding the difference between observed and predicted points. It also fits a distribution to the residual value.

plot_pmx.residual: This function plots residual for each observed value by finding the difference between observed and predicted points. It also fits a distribution to the residual value.

Description

Usage

Value

Arguments

See Also