compute_pi

Compute the prediction interval summary over time.

A generic, easy-to-use and intuitive
pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on
the R packages 'rxode2' and 'mrgsolve'. Campsis provides an
abstraction layer over the underlying processes of defining a PK/PD
model, assembling a custom dataset and running a simulation. The
package has a strong dependency on the R package 'campsismod', which
allows models to be read from and written to files, including through
a JSON-based interface, and to be adapted further on the fly in the R
environment. In addition, 'campsis' allows users to assemble datasets
in an intuitive manner, including via a JSON-based interface to import
Campsis datasets defined using formal JSON schemas distributed with
the package. Once the dataset is ready, the package prepares the
simulation, calls 'rxode2' or 'mrgsolve' (at the user's choice), and
returns the results for the given model, dataset and desired
simulation settings. The package itself is licensed under the GPL (>=
3); the JSON schema files shipped in inst/extdata are licensed
separately under the Creative Commons Attribution 4.0 International
(CC BY 4.0).

Nicolas Luyckx

campsis

Generic PK/PD Simulation Platform Campsis

compute_pi function

<dl><dt>x</dt>
<dd>data frame</dd>
<dt>variable</dt>
<dd>variable(s) used to compute the prediction interval, character vector.</dd>
<dt>strata</dt>
<dd>named vector with the strata to use, default is c(SCENARIO="all", ARM="all").</dd>
<dt>level</dt>
<dd>PI level, default is 0.9 (90% PI)</dd></dl>

Arguments

Compute the prediction interval summary over time. — compute_pi

<dl>

<dt>x</dt>
<dd>data frame</dd>


<dt>variable</dt>
<dd>variable(s) used to compute the prediction interval, character vector.</dd>


<dt>strata</dt>
<dd>named vector with the strata to use, default is c(SCENARIO="all", ARM="all").</dd>


<dt>level</dt>
<dd>PI level, default is 0.9 (90% PI)</dd>

</dl>

compute_pi: Compute the prediction interval summary over time.

Description

Usage

Value

Arguments