fitmle.cov: Computation of the Covariance Matrix

In sbihorel/scaRabee: Optimization Toolkit for Pharmacokinetic-Pharmacodynamic Models

Description

fitmle.cov is a secondary function called during estimation runs. It performs multiple tasks after completion of the model optimization by fitmle:

1- It computes the matrix of covariance (as described by D'Argenio and Schumitzky) by calling get.cov.matrix and derives some related statistics: correlation matrix, coefficient of variation of parameter estimates, confidence intervals and Akaike Information criterion,

2- It estimates secondary parameters and computes the coefficient of variation of those estimates, as well as the confidence intervals.

fitmle.cov is typically not called directly by users.

Usage

  fitmle.cov(problem = NULL,
             Fit = NULL)

Arguments

problem

A list containing the following levels: data A list containing as many levels as there are treatment levels for the subject (or population) being evaluated, plus the trts level listing all treatments for this subject (or population), and the id level giving the identification number of the subject (or set to 1 if the analysis was run at the level of the population. Each treatment-specific level is a list containing the following levels: cov mij 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) for this particular treatment. 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. 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. trt the particular treatment identifier. 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.

Fit

A list of containing the following levels: estimations The vector of final parameter estimates. fval The minimal value of the objective function.

Value

Return a list containing the following elements:

estimations

The vector of final parameter estimates.

fval

The minimal value of the objective function.

cov

The matrix of covariance for the parameter estimates.

orderedestimations

A data.frame with the same structure as problem$init but only containing the sorted estimated estimates. The sorting is performed by order.param.list.

cor

The upper triangle of the correlation matrix for the parameter estimates.

cv

The coefficients of variations for the parameter estimates.

ci

The confidence interval for the parameter estimates.

AIC

The Akaike Information Criterion.

sec

A list of data related to the secondary parameters, containing the following elements:

estimates

The vector of secondary parameter estimates calculated using the initial estimates of the primary model parameters.

estimates

The vector of secondary parameter estimates calculated using the final estimates of the primary model parameters.

names

The vector of names of the secondary parameter estimates.

pder

The matrix of partial derivatives for the secondary parameter estimates.

cov

The matrix of covariance for the secondary parameter estimates.

cv

The coefficients of variations for the secondary parameter estimates.

ci

The confidence interval for the secondary parameter estimates.

Author(s)

Sebastien Bihorel ([e-mail])

Pawel Wiczling

References

D.Z. D'Argenio and A. Schumitzky. ADAPT II User's Guide: Pharmacokinetic/ Pharmacodynamic Systems Analysis Software. Biomedical Simulations Resource, Los Angeles, 1997.

See Also

fitmle, order.parms.list

sbihorel/scaRabee documentation built on Feb. 7, 2022, 9:50 p.m.