pder: Compute Matrix of Partial Derivatives

In scaRabee: Optimization Toolkit for Pharmacokinetic-Pharmacodynamic Models

Compute Matrix of Partial Derivatives

Description

pder is a secondary function called by get.cov.matrix. It computes the matrix of partial derivatives for the model predictions and the residual variability. pder is typically not called directly by users.

Usage

  pder(subproblem = NULL,
       x = NULL)

Arguments

subproblem

A list containing the following levels: code A list of R code extracted from the model file. Depending on content of the model file, the levels of this list could be: template, derived, lags, ode, dde, output, variance, and/or secondary. method A character string, indicating the scale of the analysis. Should be 'population' or 'subject'. init A data.frame of parameter data with the following columns: 'names', 'type', 'value', 'isfix', 'lb', and 'ub'. debugmode Logical indicator of debugging mode. modfun Model function. data A list containing the following levels: xdata 1 x m matrix of time of observations of the dependent variables. data m x 3 data.frame containing the times of observations of the dependent variables (extracted from the TIME variable), the indicators of the type of dependent variables (extracted from the CMT variable), and the actual dependent variable observations (extracted from the DV variable). bolus bij x 4 data.frame providing the instantaneous inputs for a treatment and individual. infusion fij x (4+c) data.frame providing the zero-order inputs for a treatment and individual. cov mij x c data.frame containing the times of observations of the dependent variables (extracted from the TIME variable) and all the covariates identified for this particular treatment.

x

The vector of p final parameter estimates.

Value

Return a list containing the p x p matrices of partial derivatives for model predictions (mpder) and residual variability (wpder).

Author(s)

Sebastien Bihorel ([e-mail])

See Also

get.cov.matrix

scaRabee documentation built on Jan. 26, 2026, 1:07 a.m.