standardize_column

Confirm that a column is in the data, and rename data to prepare that column for later use.

Visual predictive checks are a commonly used diagnostic plot in pharmacometrics, showing how certain statistics (percentiles) for observed data compare to those same statistics for data simulated from a model. The package can generate VPCs for continuous, categorical, censored, and (repeated) time-to-event data.

Ron Keizer

Create Visual Predictive Checks

Bill Denney

standardize_column function

<dl><dt>dat</dt>
<dd>An input data.frame or similar object</dd>
<dt>cols</dt>
<dd>A list with an element for colname giving the name for colname in
<code>dat</code>.</dd>
<dt>colname</dt>
<dd>The name of the column (character scalar)</dd>
<dt>coldesc</dt>
<dd>The description of the column (character scalar)</dd>
<dt>what</dt>
<dd>The description of the data (typically "observed" or "simulated")</dd>
<dt>default</dt>
<dd>A default value (scalar or vector) to use if the column is not
found.</dd></dl>

Arguments

Confirm that a column is in the data, and rename data to prepare that column for later use. — standardize_column

<dl>

<dt>dat</dt>
<dd>An input data.frame or similar object</dd>


<dt>cols</dt>
<dd>A list with an element for colname giving the name for colname in
<code>dat</code>.</dd>


<dt>colname</dt>
<dd>The name of the column (character scalar)</dd>


<dt>coldesc</dt>
<dd>The description of the column (character scalar)</dd>


<dt>what</dt>
<dd>The description of the data (typically "observed" or "simulated")</dd>


<dt>default</dt>
<dd>A default value (scalar or vector) to use if the column is not
found.</dd>

</dl>

standardize_column: Confirm that a column is in the data, and rename data to prepare that column for later use.

Description

Usage

Value

Arguments