getsigma

Applies <code>getsigmas</code> to each individual and dose group after filtering
observation records (EVID == 0), and calculates trimmed mean estimates
of additive and proportional residual variability.

Provides automated methods for generating initial parameter estimates
in population pharmacokinetic modeling. The pipeline integrates adaptive single-point
methods, naive pooled graphic approaches, noncompartmental analysis methods, and parameter
sweeping across pharmacokinetic models. It estimates residual unexplained variability
using either data-driven or fixed-fraction approaches and assigns pragmatic initial values
for inter-individual variability. These strategies are designed to improve model
robustness and convergence in 'nlmixr2' workflows. For more details see
Huang Z, Fidler M, Lan M, Cheng IL, Kloprogge F, Standing JF (2025)
<doi:10.1007/s10928-025-10000-z>.

Zhonghui Huang

nlmixr2autoinit

Automatic Generation of Initial Estimates for Population
Pharmacokinetic Modeling

Joseph Standing

Matthew Fidler

Frank Kloprogge

getsigma function

<dl><dt>df</dt>
<dd>Full pharmacokinetic dataset containing at least the columns:
EVID, ID, TIME, DV, and routeobs.</dd>
<dt>nlastpoints</dt>
<dd>Number of terminal points used for elimination phase
regression in each group (passed to getsigmas).</dd>
<dt>sigma_trim</dt>
<dd>Trimming proportion used when calculating trimmed means
of residual standard deviations. Default is 0.05.</dd></dl>

Arguments

Author

Compute overall residual variability from elimination phase — getsigma

<dl>

<dt>df</dt>
<dd>Full pharmacokinetic dataset containing at least the columns:
EVID, ID, TIME, DV, and routeobs.</dd>


<dt>nlastpoints</dt>
<dd>Number of terminal points used for elimination phase
regression in each group (passed to getsigmas).</dd>


<dt>sigma_trim</dt>
<dd>Trimming proportion used when calculating trimmed means
of residual standard deviations. Default is 0.05.</dd>

</dl>

getsigma: Compute overall residual variability from elimination phase

Description

Usage

Value

Arguments

Author

Details

See Also

Examples