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.

Create Visual Predictive Checks

package-ubuntu-20.04-r-4.0.5#cran#vpc-1.2.2

<p>Add simulation index number to simulation when not present
</p>

add_sim_index_number(sim, id = "id", sim_label = "sim")

<table summary="R argblock">
<tr valign="top">
<td><code>sim</code></td>
<td>
<p>a data.frame containing the simulation data</p>
</td>
</tr>
<tr valign="top">
<td><code>id</code></td>
<td>
<p>character specifying the column name in the data.frame</p>
</td>
</tr>
<tr valign="top">
<td><code>sim_label</code></td>
<td>
<p>label to indicate simulation index (if available)</p>
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#vpc-1.2.2#docs#add_sim_index_number

add_sim_index_number

Description

Usage

Arguments

vpc

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`sim`	a data.frame containing the simulation data
`id`	character specifying the column name in the data.frame
`sim_label`	label to indicate simulation index (if available)