as_vpcdb

Create a vpcdb object, and standardize parameter checking

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

as_vpcdb function

<dl><dt>...</dt>
<dd>Extra parameters (not checked) added to the object</dd>
<dt>type</dt>
<dd>The type of vpc (e.g. "continuous", "categorical", "censored", or
"time-to-event")</dd>
<dt>facet</dt>
<dd>either "wrap", "columns", or "rows"</dd>
<dt>scales</dt>
<dd>Are scales shared across all facets (the default,
<code>"fixed"</code>), or do they vary across rows (<code>"free_x"</code>),
columns (<code>"free_y"</code>), or both rows and columns (<code>"free"</code>)?</dd>
<dt>labeller</dt>
<dd>ggplot2 labeller function to be passed to underlying ggplot object</dd></dl>

Arguments

Create a vpcdb object, and standardize parameter checking — as_vpcdb

<dl>

<dt>...</dt>
<dd>Extra parameters (not checked) added to the object</dd>


<dt>type</dt>
<dd>The type of vpc (e.g. "continuous", "categorical", "censored", or
"time-to-event")</dd>


<dt>facet</dt>
<dd>either "wrap", "columns", or "rows"</dd>


<dt>scales</dt>
<dd>Are scales shared across all facets (the default,
<code>"fixed"</code>), or do they vary across rows (<code>"free_x"</code>),
columns (<code>"free_y"</code>), or both rows and columns (<code>"free"</code>)?</dd>


<dt>labeller</dt>
<dd>ggplot2 labeller function to be passed to underlying ggplot object</dd>

</dl>

as_vpcdb: Create a vpcdb object, and standardize parameter checking

Description

Usage

Value

Arguments