filter_dv

Remove values that are not observed values from data

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

filter_dv function

<dl><dt>x</dt>
<dd>A data.frame or similar object</dd>
<dt>verbose</dt>
<dd>show debugging information (TRUE or FALSE)</dd>
<dt>...</dt>
<dd>Passed to software-specific filtering function</dd></dl>

Arguments

Remove values that are not observed values from data — filter_dv

<dl>

<dt>x</dt>
<dd>A data.frame or similar object</dd>


<dt>verbose</dt>
<dd>show debugging information (TRUE or FALSE)</dd>


<dt>...</dt>
<dd>Passed to software-specific filtering function</dd>

</dl>

filter_dv: Remove values that are not observed values from data

Description

Usage

Value

Arguments