sim_cat_data

A simulated dataset with the 3 levels of categorical DV across 100 replicates.

datasets

Perform a Visual Predictive Check (VPC), while accounting for
stratification, censoring, and prediction correction. Using piping from
'magrittr', the intuitive syntax gives users a flexible and powerful method
to generate VPCs using both traditional binning and a new binless approach
Jamsen et al. (2018) <doi:10.1002/psp4.12319> with Additive Quantile
Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS)
prediction correction.

James Craig

tidyvpc

VPC Percentiles and Prediction Intervals

Olivier Barriere

Benjamin Rich

Samer Mouksassi

Bill Denney

Michael Tomashevskiy

Kris Jamsen

Certara USA, Inc. 

sim_cat_data function

A data frame with 401400 rows and 4 variables:<dl>
 <dt>PID_code</dt>
<dd>Subject identifier</dd> <dt>IVAR</dt>
<dd>Time</dd> <dt>DV</dt>
<dd>Categorical DV with 3 levels</dd> <dt>Replicate</dt>
<dd>Replicate num for simulation</dd>
</dl>

Format

Example simulated data with categorical DV — sim_cat_data

A data frame with 401400 rows and 4 variables:<dl>
 <dt>PID_code</dt>
<dd>Subject identifier</dd>

 <dt>IVAR</dt>
<dd>Time</dd>

 <dt>DV</dt>
<dd>Categorical DV with 3 levels</dd>

 <dt>Replicate</dt>
<dd>Replicate num for simulation</dd>


</dl>

Example simulated data with categorical DV

sim_cat_data: Example simulated data with categorical DV

Description

Usage

Arguments

Format