pred_correction_core

Perform pred-correction for predictions that were non-zero and
were not missing observation. The latter can happen e.g. when 
censored data is set to NA in `format_vpc_input_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

pred_correction_core function

<dl><dt>data</dt>
<dd>dataset, either `sim` or `obs` data.frame</dd>
<dt>pred_col</dt>
<dd>cols$obs$pred</dd>
<dt>pred_corr_lower_bnd</dt>
<dd>lower bound for the prediction-correction</dd></dl>

Arguments

Core prediction correction function — pred_correction_core

<dl>

<dt>data</dt>
<dd>dataset, either `sim` or `obs` data.frame</dd>


<dt>pred_col</dt>
<dd>cols$obs$pred</dd>


<dt>pred_corr_lower_bnd</dt>
<dd>lower bound for the prediction-correction</dd>

</dl>

pred_correction_core: Core prediction correction function

Description

Usage

Value

Arguments