fitness_penalties_vs_iteration

Plot minimum fitness by iteration with penalty composition.

Utilize the 'shiny' interface for visualizing results from a 'pyDarwin' (<https://certara.github.io/pyDarwin/>)
machine learning pharmacometric model search. It generates Goodness-of-Fit plots and summary tables for selected models,
allowing users to customize diagnostic outputs within the interface. The underlying R code for generating plots and
tables can be extracted for use outside the interactive session. Model diagnostics can also be incorporated into an
R Markdown document and rendered in various output formats.

James Craig

Certara.DarwinReporter

Data Visualization Utilities for 'pyDarwin' Machine Learning
Pharmacometric Model Development

Michael Tomashevskiy

Mike Talley

Certara USA, Inc 

fitness_penalties_vs_iteration function

<dl><dt>darwin_data</dt>
<dd>Object of class <code>darwin_data</code>.</dd>
<dt>group_penalties</dt>
<dd>Logical; defaults to <code>TRUE</code>.</dd>
<dt>scale_ofv</dt>
<dd>Set to <code>TRUE</code> to rescale OFV axis limit. Used to better observe penalty effects.</dd>
<dt>...</dt>
<dd>Additional arguments.</dd></dl>

Arguments

Plot minimum fitness by iteration with penalty composition. — fitness_penalties_vs_iteration

<dl>

<dt>darwin_data</dt>
<dd>Object of class <code>darwin_data</code>.</dd>


<dt>group_penalties</dt>
<dd>Logical; defaults to <code>TRUE</code>.</dd>


<dt>scale_ofv</dt>
<dd>Set to <code>TRUE</code> to rescale OFV axis limit. Used to better observe penalty effects.</dd>


<dt>...</dt>
<dd>Additional arguments.</dd>

</dl>

fitness_penalties_vs_iteration: Plot minimum fitness by iteration with penalty composition.

Description

Usage

Value

Arguments