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

Draw parameters from multivariate distribution

draw_params_mvr(ids, n_sim, theta, omega_mat, par_names = NULL)

<table summary="R argblock">
<tr valign="top">
<td><code>ids</code></td>
<td>
vector of id numbers
</td>
</tr>
<tr valign="top">
<td><code>n_sim</code></td>
<td>
number of simulations
</td>
</tr>
<tr valign="top">
<td><code>theta</code></td>
<td>
theta vector
</td>
</tr>
<tr valign="top">
<td><code>omega_mat</code></td>
<td>
omega matrix
</td>
</tr>
<tr valign="top">
<td><code>par_names</code></td>
<td>
parameter names vector
</td>
</tr>
</table>

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

draw_params_mvr

Description

Usage

Arguments

vpc

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`ids`	vector of id numbers
`n_sim`	number of simulations
`theta`	theta vector
`omega_mat`	omega matrix
`par_names`	parameter names vector